diff --git a/Readme.md b/Readme.md
index 48a1773..6f95808 100644
--- a/Readme.md
+++ b/Readme.md
@@ -30,9 +30,9 @@
 |   ML application                     |  Description | Neural Network Model |
 | :----------------------------------: | :-----------------------------------------------------: | :----: |
 |  [Image classification](./docs/use_cases/img_class.md)        | Recognize the presence of objects in a given image | [Mobilenet V2](https://github.com/ARM-software/ML-zoo/tree/e0aa361b03c738047b9147d1a50e3f2dcb13dbcb/models/image_classification/mobilenet_v2_1.0_224/tflite_int8)   |
-|  [Keyword spotting(KWS)](./docs/use_cases/kws.md)             | Recognize the presence of a key word in a recording | [DS-CNN-L](https://github.com/ARM-software/ML-zoo/tree/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/keyword_spotting/ds_cnn_large/tflite_clustered_int8) |
+|  [Keyword spotting(KWS)](./docs/use_cases/kws.md)             | Recognize the presence of a key word in a recording | [MicroNet](https://github.com/ARM-software/ML-zoo/tree/9f506fe52b39df545f0e6c5ff9223f671bc5ae00/models/keyword_spotting/micronet_medium/tflite_int8) |
 |  [Automated Speech Recognition(ASR)](./docs/use_cases/asr.md) | Transcribe words in a recording | [Wav2Letter](https://github.com/ARM-software/ML-zoo/tree/1a92aa08c0de49a7304e0a7f3f59df6f4fd33ac8/models/speech_recognition/wav2letter/tflite_int8) |
-|  [KWS and ASR](./docs/use_cases/kws_asr.md) | Utilise Cortex-M and Ethos-U to transcribe words in a recording after a keyword was spotted | [DS-CNN-L](https://github.com/ARM-software/ML-zoo/tree/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/keyword_spotting/ds_cnn_large/tflite_clustered_int8)  [Wav2Letter](https://github.com/ARM-software/ML-zoo/tree/1a92aa08c0de49a7304e0a7f3f59df6f4fd33ac8/models/speech_recognition/wav2letter/tflite_int8) |
+|  [KWS and ASR](./docs/use_cases/kws_asr.md) | Utilise Cortex-M and Ethos-U to transcribe words in a recording after a keyword was spotted | [MicroNet](https://github.com/ARM-software/ML-zoo/tree/9f506fe52b39df545f0e6c5ff9223f671bc5ae00/models/keyword_spotting/micronet_medium/tflite_int8)  [Wav2Letter](https://github.com/ARM-software/ML-zoo/tree/1a92aa08c0de49a7304e0a7f3f59df6f4fd33ac8/models/speech_recognition/wav2letter/tflite_int8) |
 |  [Anomaly Detection](./docs/use_cases/ad.md)                 | Detecting abnormal behavior based on a sound recording of a machine | [MicroNet](https://github.com/ARM-software/ML-zoo/tree/7c32b097f7d94aae2cd0b98a8ed5a3ba81e66b18/models/anomaly_detection/micronet_medium/tflite_int8/)|
 |  [Visual Wake Word](./docs/use_cases/visual_wake_word.md)                 | Recognize if person is present in a given image | [MicroNet](https://github.com/ARM-software/ML-zoo/tree/7dd3b16bb84007daf88be8648983c07f3eb21140/models/visual_wake_words/micronet_vww4/tflite_int8/vww4_128_128_INT8.tflite)|
 |  [Noise Reduction](./docs/use_cases/noise_reduction.md)        | Remove noise from audio while keeping speech intact | [RNNoise](https://github.com/ARM-software/ML-zoo/raw/a061600058097a2785d6f1f7785e5a2d2a142955/models/noise_suppression/RNNoise/tflite_int8)   |
diff --git a/docs/documentation.md b/docs/documentation.md
index f1fab8c..2ec2028 100644
--- a/docs/documentation.md
+++ b/docs/documentation.md
@@ -211,7 +211,7 @@
 The models used in the use-cases implemented in this project can be downloaded from: [Arm ML-Zoo](https://github.com/ARM-software/ML-zoo).
 
 - [Mobilenet V2](https://github.com/ARM-software/ML-zoo/tree/e0aa361b03c738047b9147d1a50e3f2dcb13dbcb/models/image_classification/mobilenet_v2_1.0_224/tflite_int8)
-- [DS-CNN](https://github.com/ARM-software/ML-zoo/tree/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b//models/keyword_spotting/ds_cnn_large/tflite_clustered_int8)
+- [MicroNet for Keyword Spotting](https://github.com/ARM-software/ML-zoo/tree/9f506fe52b39df545f0e6c5ff9223f671bc5ae00/models/keyword_spotting/micronet_medium/tflite_int8)
 - [Wav2Letter](https://github.com/ARM-software/ML-zoo/tree/1a92aa08c0de49a7304e0a7f3f59df6f4fd33ac8/models/speech_recognition/wav2letter/tflite_pruned_int8)
 - [MicroNet for Anomaly Detection](https://github.com/ARM-software/ML-zoo/tree/7c32b097f7d94aae2cd0b98a8ed5a3ba81e66b18/models/anomaly_detection/micronet_medium/tflite_int8)
 - [MicroNet for Visual Wake Word](https://github.com/ARM-software/ML-zoo/raw/7dd3b16bb84007daf88be8648983c07f3eb21140/models/visual_wake_words/micronet_vww4/tflite_int8/vww4_128_128_INT8.tflite)
diff --git a/docs/quick_start.md b/docs/quick_start.md
index 6522bdd..252b084 100644
--- a/docs/quick_start.md
+++ b/docs/quick_start.md
@@ -84,11 +84,11 @@
     --output ./resources_downloaded/img_class/ifm0.npy
 curl -L https://github.com/ARM-software/ML-zoo/raw/e0aa361b03c738047b9147d1a50e3f2dcb13dbcb/models/image_classification/mobilenet_v2_1.0_224/tflite_int8/testing_output/MobilenetV2/Predictions/Reshape_11/0.npy \
     --output ./resources_downloaded/img_class/ofm0.npy
-curl -L https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/keyword_spotting/ds_cnn_large/tflite_clustered_int8/ds_cnn_clustered_int8.tflite \
-    --output ./resources_downloaded/kws/ds_cnn_clustered_int8.tflite
-curl -L https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/keyword_spotting/ds_cnn_large/tflite_clustered_int8/testing_input/input_2/0.npy \
+curl -L https://github.com/ARM-software/ML-zoo/raw/9f506fe52b39df545f0e6c5ff9223f671bc5ae00/models/keyword_spotting/micronet_medium/tflite_int8/kws_micronet_m.tflite \
+    --output ./resources_downloaded/kws/kws_micronet_m.tflite
+curl -L https://github.com/ARM-software/ML-zoo/raw/9f506fe52b39df545f0e6c5ff9223f671bc5ae00/models/keyword_spotting/micronet_medium/tflite_int8/testing_input/input/0.npy \
     --output ./resources_downloaded/kws/ifm0.npy
-curl -L https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/keyword_spotting/ds_cnn_large/tflite_clustered_int8/testing_output/Identity/0.npy \
+curl -L https://github.com/ARM-software/ML-zoo/raw/9f506fe52b39df545f0e6c5ff9223f671bc5ae00/models/keyword_spotting/micronet_medium/tflite_int8/testing_output/Identity/0.npy \
     --output ./resources_downloaded/kws/ofm0.npy
 curl -L https://github.com/ARM-software/ML-zoo/raw/1a92aa08c0de49a7304e0a7f3f59df6f4fd33ac8/models/speech_recognition/wav2letter/tflite_pruned_int8/wav2letter_pruned_int8.tflite \
     --output ./resources_downloaded/kws_asr/wav2letter_pruned_int8.tflite
@@ -96,11 +96,11 @@
     --output ./resources_downloaded/kws_asr/asr/ifm0.npy
 curl -L https://github.com/ARM-software/ML-zoo/raw/1a92aa08c0de49a7304e0a7f3f59df6f4fd33ac8/models/speech_recognition/wav2letter/tflite_pruned_int8/testing_output/Identity_int8/0.npy \
     --output ./resources_downloaded/kws_asr/asr/ofm0.npy
-curl -L https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/keyword_spotting/ds_cnn_large/tflite_clustered_int8/ds_cnn_clustered_int8.tflite \
-    --output ./resources_downloaded/kws_asr/ds_cnn_clustered_int8.tflite
-curl -L https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/keyword_spotting/ds_cnn_large/tflite_clustered_int8/testing_input/input_2/0.npy \
+curl -L https://github.com/ARM-software/ML-zoo/raw/9f506fe52b39df545f0e6c5ff9223f671bc5ae00/models/keyword_spotting/micronet_medium/tflite_int8/kws_micronet_m.tflite \
+    --output ./resources_downloaded/kws_asr/kws_micronet_m.tflite
+curl -L https://github.com/ARM-software/ML-zoo/raw/9f506fe52b39df545f0e6c5ff9223f671bc5ae00/models/keyword_spotting/micronet_medium/tflite_int8/testing_input/input/0.npy \
     --output ./resources_downloaded/kws_asr/kws/ifm0.npy
-curl -L https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/keyword_spotting/ds_cnn_large/tflite_clustered_int8/testing_output/Identity/0.npy \
+curl -L https://github.com/ARM-software/ML-zoo/raw/9f506fe52b39df545f0e6c5ff9223f671bc5ae00/models/keyword_spotting/micronet_medium/tflite_int8/testing_output/Identity/0.npy \
     --output ./resources_downloaded/kws_asr/kws/ofm0.npy
 curl -L https://github.com/ARM-software/ML-zoo/raw/a061600058097a2785d6f1f7785e5a2d2a142955/models/noise_suppression/RNNoise/tflite_int8/rnnoise_INT8.tflite \
     --output ./resources_downloaded/noise_reduction/rnnoise_INT8.tflite
@@ -131,22 +131,22 @@
 curl -L https://github.com/ARM-software/ML-zoo/raw/7dd3b16bb84007daf88be8648983c07f3eb21140/models/visual_wake_words/micronet_vww4/tflite_int8/testing_output/Identity/0.npy \
     --output ./resources_downloaded/vww/ofm0.npy
 
-. resources_downloaded/env/bin/activate && vela resources_downloaded/kws/ds_cnn_clustered_int8.tflite \
+. resources_downloaded/env/bin/activate && vela resources_downloaded/kws/kws_micronet_m.tflite \
     --accelerator-config=ethos-u55-128 \
     --optimise Performance --config scripts/vela/default_vela.ini \
     --memory-mode=Shared_Sram \
     --system-config=Ethos_U55_High_End_Embedded \
     --output-dir=resources_downloaded/kws \
     --arena-cache-size=2097152
-mv resources_downloaded/kws/ds_cnn_clustered_int8_vela.tflite resources_downloaded/kws/ds_cnn_clustered_int8_vela_H128.tflite
+mv resources_downloaded/kws/kws_micronet_m.tflite resources_downloaded/kws/kws_micronet_m_vela_H128.tflite
 
-. resources_downloaded/env/bin/activate && vela resources_downloaded/kws/ds_cnn_clustered_int8.tflite \
+. resources_downloaded/env/bin/activate && vela resources_downloaded/kws/kws_micronet_m.tflite \
     --accelerator-config=ethos-u65-256 \
     --optimise Performance --config scripts/vela/default_vela.ini \
     --memory-mode=Dedicated_Sram \
     --system-config=Ethos_U65_High_End \
     --output-dir=resources_downloaded/kws
-mv resources_downloaded/kws/ds_cnn_clustered_int8_vela.tflite resources_downloaded/kws/ds_cnn_clustered_int8_vela_Y256.tflite
+mv resources_downloaded/kws/kws_micronet_vela.tflite resources_downloaded/kws/kws_micronet_m_vela_Y256.tflite
 
 . resources_downloaded/env/bin/activate && vela resources_downloaded/kws_asr/wav2letter_int8.tflite \
     --accelerator-config=ethos-u55-128 \
@@ -165,22 +165,22 @@
     --output-dir=resources_downloaded/kws_asr
 mv resources_downloaded/kws_asr/wav2letter_int8_vela.tflite resources_downloaded/kws_asr/wav2letter_int8_vela_Y256.tflite
 
-. resources_downloaded/env/bin/activate && vela resources_downloaded/kws_asr/ds_cnn_clustered_int8.tflite \
+. resources_downloaded/env/bin/activate && vela resources_downloaded/kws_asr/kws_micronet_m.tflite \
     --accelerator-config=ethos-u55-128 \
     --optimise Performance --config scripts/vela/default_vela.ini \
     --memory-mode=Shared_Sram \
     --system-config=Ethos_U55_High_End_Embedded \
     --output-dir=resources_downloaded/kws_asr \
     --arena-cache-size=2097152
-mv resources_downloaded/kws_asr/ds_cnn_clustered_int8_vela.tflite resources_downloaded/kws_asr/ds_cnn_clustered_int8_vela_H128.tflite
+mv resources_downloaded/kws_asr/kws_micronet_m.tflite_vela.tflite resources_downloaded/kws_asr/kws_micronet_m.tflite_vela_H128.tflite
 
-. resources_downloaded/env/bin/activate && vela resources_downloaded/kws_asr/ds_cnn_clustered_int8.tflite \
+. resources_downloaded/env/bin/activate && vela resources_downloaded/kws_asr/kws_micronet_m.tflite \
     --accelerator-config=ethos-u65-256 \
     --optimise Performance --config scripts/vela/default_vela.ini \
     --memory-mode=Dedicated_Sram \
     --system-config=Ethos_U65_High_End \
     --output-dir=resources_downloaded/kws_asr
-mv resources_downloaded/kws_asr/ds_cnn_clustered_int8_vela.tflite resources_downloaded/kws_asr/ds_cnn_clustered_int8_vela_Y256.tflite
+mv resources_downloaded/kws_asr/kws_micronet_m.tflite_vela.tflite resources_downloaded/kws_asr/kws_micronet_m.tflite_vela_Y256.tflite
 
 . resources_downloaded/env/bin/activate && vela resources_downloaded/inference_runner/dnn_s_quantized.tflite \
     --accelerator-config=ethos-u55-128 \
diff --git a/docs/use_cases/kws.md b/docs/use_cases/kws.md
index 84eddd6..0c50fe5 100644
--- a/docs/use_cases/kws.md
+++ b/docs/use_cases/kws.md
@@ -23,7 +23,7 @@
 
 ### Preprocessing and feature extraction
 
-The `DS-CNN` keyword spotting model that is used with the Code Samples expects audio data to be preprocessed in a
+The `MicroNet` keyword spotting model that is used with the Code Samples expects audio data to be preprocessed in a
 specific way before performing an inference.
 
 Therefore, this section aims to provide an overview of the feature extraction process used.
@@ -62,7 +62,7 @@
 ### Postprocessing
 
 After an inference is complete, the word with the highest detected probability is output to console. Providing that the
-probability is larger than a threshold value. The default is set to `0.9`.
+probability is larger than a threshold value. The default is set to `0.7`.
 
 If multiple inferences are performed for an audio clip, then multiple results are output.
 
@@ -107,7 +107,7 @@
   this number, then it is padded with zeros. The default value is `16000`.
 
 - `kws_MODEL_SCORE_THRESHOLD`: Threshold value that must be applied to the inference results for a label to be deemed
-  valid. Goes from 0.00 to 1.0. The default is `0.9`.
+  valid. Goes from 0.00 to 1.0. The default is `0.7`.
 
 - `kws_ACTIVATION_BUF_SZ`: The intermediate, or activation, buffer size reserved for the NN model. By default, it is set
   to 2MiB and is enough for most models
@@ -247,7 +247,7 @@
 
 To run the application with a custom model, you must provide a `labels_<model_name>.txt` file of labels that are
 associated with the model. Each line of the file must correspond to one of the outputs in your model. Refer to the
-provided `ds_cnn_labels.txt` file for an example.
+provided `micronet_kws_labels.txt` file for an example.
 
 Then, you must set `kws_MODEL_TFLITE_PATH` to the location of the Vela processed model file and `kws_LABELS_TXT_FILE`to
 the location of the associated labels file.
@@ -369,24 +369,24 @@
     INFO - Model INPUT tensors:
     INFO -  tensor type is INT8
     INFO -  tensor occupies 490 bytes with dimensions
-    INFO -    0:   1
-    INFO -    1:   1
-    INFO -    2:  49
-    INFO -    3:  10
+    INFO - 		0:   1
+    INFO - 		1:  49
+    INFO - 		2:  10
+    INFO - 		3:   1
     INFO - Quant dimension: 0
-    INFO - Scale[0] = 1.107164
-    INFO - ZeroPoint[0] = 95
+    INFO - Scale[0] = 0.201095
+    INFO - ZeroPoint[0] = -5
     INFO - Model OUTPUT tensors:
-    INFO -  tensor type is INT8
-    INFO -  tensor occupies 12 bytes with dimensions
-    INFO -    0:   1
-    INFO -    1:  12
+    INFO - 	tensor type is INT8
+    INFO - 	tensor occupies 12 bytes with dimensions
+    INFO - 		0:   1
+    INFO - 		1:  12
     INFO - Quant dimension: 0
-    INFO - Scale[0] = 0.003906
-    INFO - ZeroPoint[0] = -128
-    INFO - Activation buffer (a.k.a tensor arena) size used: 72848
-    INFO - Number of operators: 1
-    INFO -  Operator 0: ethos-u
+    INFO - Scale[0] = 0.056054
+    INFO - ZeroPoint[0] = -54
+    INFO - Activation buffer (a.k.a tensor arena) size used: 127068
+    INFO - Number of operators: 0
+    INFO - 	Operator 0: ethos-u
     ```
 
 5. List audio clips: Prints a list of pair ... indexes. The original filenames are embedded in the application, like so:
@@ -405,18 +405,21 @@
 
 The following example illustrates the output for classification:
 
-```logINFO - Running inference on audio clip 0 => down.wav
+```log
+
+INFO - Running inference on audio clip 0 => down.wav
 INFO - Inference 1/1
 INFO - Final results:
 INFO - Total number of inferences: 1
-INFO - For timestamp: 0.000000 (inference #: 0); label: down, score: 0.996094; threshold: 0.900000
+INFO - For timestamp: 0.000000 (inference #: 0); label: down, score: 0.986182; threshold: 0.700000
 INFO - Profile for Inference:
-INFO - NPU AXI0_RD_DATA_BEAT_RECEIVED beats: 217385
-INFO - NPU AXI0_WR_DATA_BEAT_WRITTEN beats: 82607
-INFO - NPU AXI1_RD_DATA_BEAT_RECEIVED beats: 59608
-INFO - NPU ACTIVE cycles: 680611
-INFO - NPU IDLE cycles: 561
-INFO - NPU TOTAL cycles: 681172
+INFO - NPU AXI0_RD_DATA_BEAT_RECEIVED beats: 132130
+INFO - NPU AXI0_WR_DATA_BEAT_WRITTEN beats: 48252
+INFO - NPU AXI1_RD_DATA_BEAT_RECEIVED beats: 17544
+INFO - NPU ACTIVE cycles: 413814
+INFO - NPU IDLE cycles: 358
+INFO - NPU TOTAL cycles: 414172
+
 ```
 
 On most systems running Fast Model, each inference takes under 30 seconds.
@@ -425,22 +428,22 @@
 
 - *Ethos-U* PMU report:
 
-  - 681,172 total cycle: The number of NPU cycles.
+  - 414,172 total cycle: The number of NPU cycles.
 
-  - 680,611 active cycles: The number of NPU cycles that were used for computation.
+  - 413,814 active cycles: The number of NPU cycles that were used for computation.
 
-  - 561 idle cycles: The number of cycles for which the NPU was idle.
+  - 358 idle cycles: The number of cycles for which the NPU was idle.
 
-  - 217,385 AXI0 read beats: The number of AXI beats with read transactions from the AXI0 bus. AXI0 is the bus where the
+  - 132,130 AXI0 read beats: The number of AXI beats with read transactions from the AXI0 bus. AXI0 is the bus where the
     *Ethos-U* NPU reads and writes to the computation buffers, activation buf, or tensor arenas.
 
-  - 82,607 write cycles: The number of AXI beats with write transactions to AXI0 bus.
+  - 48,252 write cycles: The number of AXI beats with write transactions to AXI0 bus.
 
-  - 59,608 AXI1 read beats: The number of AXI beats with read transactions from the AXI1 bus. AXI1 is the bus where the
+  - 17,544 AXI1 read beats: The number of AXI beats with read transactions from the AXI1 bus. AXI1 is the bus where the
     *Ethos-U* NPU reads the model. So, read-only.
 
 - For FPGA platforms, a CPU cycle count can also be enabled. However, do not use cycle counters for FVP, as the CPU
   model is not cycle-approximate or cycle-accurate.
 
-> **Note:** The application prints the highest confidence score and the associated label from the `ds_cnn_labels.txt`
+> **Note:** The application prints the highest confidence score and the associated label from the `micronet_kws_labels.txt`
 > file.
diff --git a/docs/use_cases/kws_asr.md b/docs/use_cases/kws_asr.md
index 42c9d3a..22f1e9d 100644
--- a/docs/use_cases/kws_asr.md
+++ b/docs/use_cases/kws_asr.md
@@ -44,7 +44,7 @@
 
 #### Keyword Spotting Preprocessing
 
-The `DS-CNN` keyword spotting model that is used with the Code Samples expects audio data to be preprocessed in a
+The `MicroNet` keyword spotting model that is used with the Code Samples expects audio data to be preprocessed in a
 specific way before performing an inference.
 
 Therefore, this section aims to provide an overview of the feature extraction process used.
@@ -455,43 +455,30 @@
 
 4. Show NN model info: Prints information about the model data type, input, and output, tensor sizes:
 
-    ```log
+   ```log
+    INFO - Model info:
     INFO - Model INPUT tensors:
     INFO -  tensor type is INT8
     INFO -  tensor occupies 490 bytes with dimensions
-    INFO -    0:   1
-    INFO -    1:   1
-    INFO -    2:  49
-    INFO -    3:  10
+    INFO - 		0:   1
+    INFO - 		1:  49
+    INFO - 		2:  10
+    INFO - 		3:   1
     INFO - Quant dimension: 0
-    INFO - Scale[0] = 1.107164
-    INFO - ZeroPoint[0] = 95
+    INFO - Scale[0] = 0.201095
+    INFO - ZeroPoint[0] = -5
     INFO - Model OUTPUT tensors:
-    INFO -  tensor type is INT8
-    INFO -  tensor occupies 12 bytes with dimensions
-    INFO -    0:   1
-    INFO -    1:  12
+    INFO - 	tensor type is INT8
+    INFO - 	tensor occupies 12 bytes with dimensions
+    INFO - 		0:   1
+    INFO - 		1:  12
     INFO - Quant dimension: 0
-    INFO - Scale[0] = 0.003906
-    INFO - ZeroPoint[0] = -128
-    INFO - Activation buffer (a.k.a tensor arena) size used: 123616
-    INFO - Number of operators: 16
-    INFO -  Operator 0: RESHAPE
-    INFO -  Operator 1: CONV_2D
-    INFO -  Operator 2: DEPTHWISE_CONV_2D
-    INFO -  Operator 3: CONV_2D
-    INFO -  Operator 4: DEPTHWISE_CONV_2D
-    INFO -  Operator 5: CONV_2D
-    INFO -  Operator 6: DEPTHWISE_CONV_2D
-    INFO -  Operator 7: CONV_2D
-    INFO -  Operator 8: DEPTHWISE_CONV_2D
-    INFO -  Operator 9: CONV_2D
-    INFO -  Operator 10: DEPTHWISE_CONV_2D
-    INFO -  Operator 11: CONV_2D
-    INFO -  Operator 12: AVERAGE_POOL_2D
-    INFO -  Operator 13: RESHAPE
-    INFO -  Operator 14: FULLY_CONNECTED
-    INFO -  Operator 15: SOFTMAX
+    INFO - Scale[0] = 0.056054
+    INFO - ZeroPoint[0] = -54
+    INFO - Activation buffer (a.k.a tensor arena) size used: 127068
+    INFO - Number of operators: 1
+    INFO -  Operator 0: ethos-u
+   
     INFO - Model INPUT tensors:
     INFO -  tensor type is INT8
     INFO -  tensor occupies 11544 bytes with dimensions
@@ -511,9 +498,9 @@
     INFO - Quant dimension: 0
     INFO - Scale[0] = 0.003906
     INFO - ZeroPoint[0] = -128
-    INFO - Activation buffer (a.k.a tensor arena) size used: 809808
+    INFO - Activation buffer (a.k.a tensor arena) size used: 4184332
     INFO - Number of operators: 1
-    INFO -  Operator 0: ethos-u
+    INFO - Operator 0: ethos-u
     ```
 
 5. List audio clips: Prints a list of pair ... indexes. The original filenames are embedded in the application, like so:
@@ -534,27 +521,31 @@
 INFO - Running KWS inference on audio clip 0 => yes_no_go_stop.wav
 INFO - Inference 1/7
 INFO - For timestamp: 0.000000 (inference #: 0); threshold: 0.900000
-INFO -          label @ 0: yes, score: 0.996094
+INFO -          label @ 0: yes, score: 0.997407
 INFO - Profile for Inference:
-INFO - NPU AXI0_RD_DATA_BEAT_RECEIVED beats: 217385
-INFO - NPU AXI0_WR_DATA_BEAT_WRITTEN beats: 82607
-INFO - NPU AXI1_RD_DATA_BEAT_RECEIVED beats: 59608
-INFO - NPU ACTIVE cycles: 680611
-INFO - NPU IDLE cycles: 561
-INFO - NPU TOTAL cycles: 681172
+INFO - NPU AXI0_RD_DATA_BEAT_RECEIVED beats: 132130
+INFO - NPU AXI0_WR_DATA_BEAT_WRITTEN beats: 48252
+INFO - NPU AXI1_RD_DATA_BEAT_RECEIVED beats: 17544
+INFO - NPU ACTIVE cycles: 413814
+INFO - NPU IDLE cycles: 358
+INFO - NPU TOTAL cycles: 414172
 INFO - Keyword spotted
 INFO - Inference 1/2
 INFO - Inference 2/2
-INFO - Result for inf 0: no gow
-INFO - Result for inf 1:  stoppe
-INFO - Final result: no gow stoppe
+INFO - Result for inf 0: no go
+INFO - Result for inf 1:  stop
+INFO - Final result: no go stop
 INFO - Profile for Inference:
-INFO - NPU AXI0_RD_DATA_BEAT_RECEIVED beats: 13520864
-INFO - NPU AXI0_WR_DATA_BEAT_WRITTEN beats: 2841970
-INFO - NPU AXI1_RD_DATA_BEAT_RECEIVED beats: 2717670
-INFO - NPU ACTIVE cycles: 28909309
-INFO - NPU IDLE cycles: 863
-INFO - NPU TOTAL cycles: 28910172
+INFO - NPU AXI0_RD_DATA_BEAT_RECEIVED beats: 8895431
+INFO - NPU AXI0_WR_DATA_BEAT_WRITTEN beats: 1890168
+INFO - NPU AXI1_RD_DATA_BEAT_RECEIVED beats: 1740069
+INFO - NPU ACTIVE cycles: 30164330
+INFO - NPU IDLE cycles: 342
+INFO - NPU TOTAL cycles: 30164672
+INFO - Main loop terminated.
+INFO - program terminating...
+INFO - releasing platform Arm Corstone-300 (SSE-300)
+
 ```
 
 It can take several minutes to complete one inference run. The average time is around 2-3 minutes.
@@ -567,22 +558,21 @@
 
 - *Ethos-U* PMU report:
 
-  - 28,910,172 total cycle: The number of NPU cycles.
+  - 30,164,672 total cycle: The number of NPU cycles.
 
-  - 28,909,309 active cycles: The number of NPU cycles that were used for computation.
+  - 30,164,330 active cycles: The number of NPU cycles that were used for computation.
 
-  - 863 idle cycles: The number of cycles for which the NPU was idle.
+  - 342 idle cycles: The number of cycles for which the NPU was idle.
 
-  - 13,520,864 AXI0 read beats: The number of AXI beats with read transactions from the AXI0 bus. AXI0 is the bus where
+  - 8,895,431 AXI0 read beats: The number of AXI beats with read transactions from the AXI0 bus. AXI0 is the bus where
     the *Ethos-U* NPU reads and writes to the computation buffers, activation buf, or tensor arenas.
 
-  - 2,841,970 AXI0 write beats: The number of AXI beats with write transactions to AXI0 bus.
+  - 1,890,168 AXI0 write beats: The number of AXI beats with write transactions to AXI0 bus.
 
-  - 2,717,670 AXI1 read beats: The number of AXI beats with read transactions from the AXI1 bus. AXI1 is the bus where
+  - 1,740,069 AXI1 read beats: The number of AXI beats with read transactions from the AXI1 bus. AXI1 is the bus where
     the *Ethos-U55* NPU reads the model. So, read-only.
 
 - For FPGA platforms, a CPU cycle count can also be enabled. However, do not use cycle counters for FVP, as the CPU
   model is not cycle-approximate or cycle-accurate.
 
-> **Note:** In this example, the KWS inference does *not* use the *Ethos-U55* and only runs on the CPU. Therefore, `0`
-> Active NPU cycles are shown.
+
diff --git a/resources/kws/labels/ds_cnn_labels.txt b/resources/kws/labels/micronet_kws_labels.txt
similarity index 98%
rename from resources/kws/labels/ds_cnn_labels.txt
rename to resources/kws/labels/micronet_kws_labels.txt
index ba41645..7ad7488 100644
--- a/resources/kws/labels/ds_cnn_labels.txt
+++ b/resources/kws/labels/micronet_kws_labels.txt
@@ -1,12 +1,12 @@
+down
+go
+left
+no
+off
+on
+right
+stop
+up
+yes
 _silence_
 _unknown_
-yes
-no
-up
-down
-left
-right
-on
-off
-stop
-go
\ No newline at end of file
diff --git a/resources/kws_asr/labels/ds_cnn_labels.txt b/resources/kws_asr/labels/micronet_kws_labels.txt
similarity index 98%
rename from resources/kws_asr/labels/ds_cnn_labels.txt
rename to resources/kws_asr/labels/micronet_kws_labels.txt
index ba41645..7ad7488 100644
--- a/resources/kws_asr/labels/ds_cnn_labels.txt
+++ b/resources/kws_asr/labels/micronet_kws_labels.txt
@@ -1,12 +1,12 @@
+down
+go
+left
+no
+off
+on
+right
+stop
+up
+yes
 _silence_
 _unknown_
-yes
-no
-up
-down
-left
-right
-on
-off
-stop
-go
\ No newline at end of file
diff --git a/set_up_default_resources.py b/set_up_default_resources.py
index 91007e4..d244213 100755
--- a/set_up_default_resources.py
+++ b/set_up_default_resources.py
@@ -56,12 +56,12 @@
     },
     {
         "use_case_name": "kws",
-        "resources": [{"name": "ds_cnn_clustered_int8.tflite",
-                       "url": "https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/keyword_spotting/ds_cnn_large/tflite_clustered_int8/ds_cnn_clustered_int8.tflite"},
-                      {"name": "ifm0.npy",
-                       "url": "https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/keyword_spotting/ds_cnn_large/tflite_clustered_int8/testing_input/input_2/0.npy"},
+        "resources": [{"name": "ifm0.npy",
+                       "url": "https://github.com/ARM-software/ML-zoo/raw/9f506fe52b39df545f0e6c5ff9223f671bc5ae00/models/keyword_spotting/micronet_medium/tflite_int8/testing_input/input/0.npy"},
                       {"name": "ofm0.npy",
-                       "url": "https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/keyword_spotting/ds_cnn_large/tflite_clustered_int8/testing_output/Identity/0.npy"}]
+                       "url": "https://github.com/ARM-software/ML-zoo/raw/9f506fe52b39df545f0e6c5ff9223f671bc5ae00/models/keyword_spotting/micronet_medium/tflite_int8/testing_output/Identity/0.npy"},
+                      {"name": "kws_micronet_m.tflite",
+                       "url": " https://github.com/ARM-software/ML-zoo/raw/9f506fe52b39df545f0e6c5ff9223f671bc5ae00/models/keyword_spotting/micronet_medium/tflite_int8/kws_micronet_m.tflite"}]
     },
      {
         "use_case_name": "vww",
@@ -80,12 +80,12 @@
                        "url": "https://github.com/ARM-software/ML-zoo/raw/1a92aa08c0de49a7304e0a7f3f59df6f4fd33ac8/models/speech_recognition/wav2letter/tflite_pruned_int8/testing_input/input_2_int8/0.npy"},
                       {"sub_folder": "asr", "name": "ofm0.npy",
                        "url": "https://github.com/ARM-software/ML-zoo/raw/1a92aa08c0de49a7304e0a7f3f59df6f4fd33ac8/models/speech_recognition/wav2letter/tflite_pruned_int8/testing_output/Identity_int8/0.npy"},
-                      {"name": "ds_cnn_clustered_int8.tflite",
-                       "url": "https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/keyword_spotting/ds_cnn_large/tflite_clustered_int8/ds_cnn_clustered_int8.tflite"},
                       {"sub_folder": "kws", "name": "ifm0.npy",
-                       "url": "https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/keyword_spotting/ds_cnn_large/tflite_clustered_int8/testing_input/input_2/0.npy"},
+                       "url": "https://github.com/ARM-software/ML-zoo/raw/9f506fe52b39df545f0e6c5ff9223f671bc5ae00/models/keyword_spotting/micronet_medium/tflite_int8/testing_input/input/0.npy"},
                       {"sub_folder": "kws", "name": "ofm0.npy",
-                       "url": "https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/keyword_spotting/ds_cnn_large/tflite_clustered_int8/testing_output/Identity/0.npy"}]
+                       "url": "https://github.com/ARM-software/ML-zoo/raw/9f506fe52b39df545f0e6c5ff9223f671bc5ae00/models/keyword_spotting/micronet_medium/tflite_int8/testing_output/Identity/0.npy"},
+                      {"name": "kws_micronet_m.tflite",
+                       "url": "https://github.com/ARM-software/ML-zoo/raw/9f506fe52b39df545f0e6c5ff9223f671bc5ae00/models/keyword_spotting/micronet_medium/tflite_int8/kws_micronet_m.tflite"}]
     },
     {
         "use_case_name": "noise_reduction",
@@ -303,8 +303,8 @@
     # 3. Run vela on models in resources_downloaded
     # New models will have same name with '_vela' appended.
     # For example:
-    #   original model:  ds_cnn_clustered_int8.tflite
-    # after vela model:  ds_cnn_clustered_int8_vela_H128.tflite
+    # original model:    kws_micronet_m.tflite
+    # after vela model:  kws_micronet_m_vela_H128.tflite
     #
     # Note: To avoid to run vela twice on the same model, it's supposed that
     # downloaded model names don't contain the 'vela' word.
diff --git a/source/application/main/Classifier.cc b/source/application/main/Classifier.cc
index c5519fb..a6ff532 100644
--- a/source/application/main/Classifier.cc
+++ b/source/application/main/Classifier.cc
@@ -24,61 +24,40 @@
 #include <set>
 #include <cstdint>
 #include <inttypes.h>
+#include "PlatformMath.hpp"
 
 namespace arm {
 namespace app {
 
-    template<typename T>
-    void SetVectorResults(std::set<std::pair<T, uint32_t>>& topNSet,
+    void Classifier::SetVectorResults(std::set<std::pair<float, uint32_t>>& topNSet,
                           std::vector<ClassificationResult>& vecResults,
-                          TfLiteTensor* tensor,
-                          const std::vector <std::string>& labels) {
-
-        /* For getting the floating point values, we need quantization parameters. */
-        QuantParams quantParams = GetTensorQuantParams(tensor);
+                          const std::vector <std::string>& labels)
+    {
 
         /* Reset the iterator to the largest element - use reverse iterator. */
-        auto topNIter = topNSet.rbegin();
-        for (size_t i = 0; i < vecResults.size() && topNIter != topNSet.rend(); ++i, ++topNIter) {
-            T score = topNIter->first;
-            vecResults[i].m_normalisedVal = quantParams.scale * (score - quantParams.offset);
-            vecResults[i].m_label = labels[topNIter->second];
-            vecResults[i].m_labelIdx = topNIter->second;
-        }
 
-    }
-
-    template<>
-    void SetVectorResults<float>(std::set<std::pair<float, uint32_t>>& topNSet,
-                          std::vector<ClassificationResult>& vecResults,
-                          TfLiteTensor* tensor,
-                          const std::vector <std::string>& labels) {
-        UNUSED(tensor);
-        /* Reset the iterator to the largest element - use reverse iterator. */
         auto topNIter = topNSet.rbegin();
         for (size_t i = 0; i < vecResults.size() && topNIter != topNSet.rend(); ++i, ++topNIter) {
             vecResults[i].m_normalisedVal = topNIter->first;
             vecResults[i].m_label = labels[topNIter->second];
             vecResults[i].m_labelIdx = topNIter->second;
         }
-
     }
 
-    template<typename T>
-    bool Classifier::GetTopNResults(TfLiteTensor* tensor,
+    bool Classifier::GetTopNResults(const std::vector<float>& tensor,
                                     std::vector<ClassificationResult>& vecResults,
                                     uint32_t topNCount,
                                     const std::vector <std::string>& labels)
     {
-        std::set<std::pair<T, uint32_t>> sortedSet;
+
+        std::set<std::pair<float , uint32_t>> sortedSet;
 
         /* NOTE: inputVec's size verification against labels should be
          *       checked by the calling/public function. */
-        T* tensorData = tflite::GetTensorData<T>(tensor);
 
         /* Set initial elements. */
         for (uint32_t i = 0; i < topNCount; ++i) {
-            sortedSet.insert({tensorData[i], i});
+            sortedSet.insert({tensor[i], i});
         }
 
         /* Initialise iterator. */
@@ -86,33 +65,26 @@
 
         /* Scan through the rest of elements with compare operations. */
         for (uint32_t i = topNCount; i < labels.size(); ++i) {
-            if (setFwdIter->first < tensorData[i]) {
+            if (setFwdIter->first < tensor[i]) {
                 sortedSet.erase(*setFwdIter);
-                sortedSet.insert({tensorData[i], i});
+                sortedSet.insert({tensor[i], i});
                 setFwdIter = sortedSet.begin();
             }
         }
 
         /* Final results' container. */
         vecResults = std::vector<ClassificationResult>(topNCount);
-
-        SetVectorResults<T>(sortedSet, vecResults, tensor, labels);
+        SetVectorResults(sortedSet, vecResults, labels);
 
         return true;
     }
 
-    template bool  Classifier::GetTopNResults<uint8_t>(TfLiteTensor* tensor,
-                                                       std::vector<ClassificationResult>& vecResults,
-                                                       uint32_t topNCount, const std::vector <std::string>& labels);
-
-    template bool  Classifier::GetTopNResults<int8_t>(TfLiteTensor* tensor,
-                                                      std::vector<ClassificationResult>& vecResults,
-                                                      uint32_t topNCount, const std::vector <std::string>& labels);
-
     bool  Classifier::GetClassificationResults(
         TfLiteTensor* outputTensor,
         std::vector<ClassificationResult>& vecResults,
-        const std::vector <std::string>& labels, uint32_t topNCount)
+        const std::vector <std::string>& labels,
+        uint32_t topNCount,
+        bool useSoftmax)
     {
         if (outputTensor == nullptr) {
             printf_err("Output vector is null pointer.\n");
@@ -120,7 +92,7 @@
         }
 
         uint32_t totalOutputSize = 1;
-        for (int inputDim = 0; inputDim < outputTensor->dims->size; inputDim++){
+        for (int inputDim = 0; inputDim < outputTensor->dims->size; inputDim++) {
             totalOutputSize *= outputTensor->dims->data[inputDim];
         }
 
@@ -139,22 +111,52 @@
         bool resultState;
         vecResults.clear();
 
-        /* Get the top N results. */
+        /* De-Quantize Output Tensor */
+        QuantParams quantParams = GetTensorQuantParams(outputTensor);
+
+        /* Floating point tensor data to be populated
+         * NOTE: The assumption here is that the output tensor size isn't too
+         * big and therefore, there's neglibible impact on heap usage. */
+        std::vector<float> tensorData(totalOutputSize);
+
+        /* Populate the floating point buffer */
         switch (outputTensor->type) {
-            case kTfLiteUInt8:
-                resultState = GetTopNResults<uint8_t>(outputTensor, vecResults, topNCount, labels);
+            case kTfLiteUInt8: {
+                uint8_t *tensor_buffer = tflite::GetTensorData<uint8_t>(outputTensor);
+                for (size_t i = 0; i < totalOutputSize; ++i) {
+                    tensorData[i] = quantParams.scale *
+                        (static_cast<float>(tensor_buffer[i]) - quantParams.offset);
+                }
                 break;
-            case kTfLiteInt8:
-                resultState = GetTopNResults<int8_t>(outputTensor, vecResults, topNCount, labels);
+            }
+            case kTfLiteInt8: {
+                int8_t *tensor_buffer = tflite::GetTensorData<int8_t>(outputTensor);
+                for (size_t i = 0; i < totalOutputSize; ++i) {
+                    tensorData[i] = quantParams.scale *
+                        (static_cast<float>(tensor_buffer[i]) - quantParams.offset);
+                }
                 break;
-            case kTfLiteFloat32:
-                resultState = GetTopNResults<float>(outputTensor, vecResults, topNCount, labels);
+            }
+            case kTfLiteFloat32: {
+                float *tensor_buffer = tflite::GetTensorData<float>(outputTensor);
+                for (size_t i = 0; i < totalOutputSize; ++i) {
+                    tensorData[i] = tensor_buffer[i];
+                }
                 break;
+            }
             default:
-                printf_err("Tensor type %s not supported by classifier\n", TfLiteTypeGetName(outputTensor->type));
+                printf_err("Tensor type %s not supported by classifier\n",
+                    TfLiteTypeGetName(outputTensor->type));
                 return false;
         }
 
+        if (useSoftmax) {
+            math::MathUtils::SoftmaxF32(tensorData);
+        }
+
+        /* Get the top N results. */
+        resultState = GetTopNResults(tensorData, vecResults, topNCount, labels);
+
         if (!resultState) {
             printf_err("Failed to get top N results set\n");
             return false;
@@ -162,6 +164,5 @@
 
         return true;
     }
-
 } /* namespace app */
 } /* namespace arm */
\ No newline at end of file
diff --git a/source/application/main/PlatformMath.cc b/source/application/main/PlatformMath.cc
index 0b8882a..26b4b72 100644
--- a/source/application/main/PlatformMath.cc
+++ b/source/application/main/PlatformMath.cc
@@ -15,6 +15,8 @@
  * limitations under the License.
  */
 #include "PlatformMath.hpp"
+#include <algorithm>
+#include <numeric>
 
 #if 0 == ARM_DSP_AVAILABLE
     #include <cmath>
@@ -290,6 +292,24 @@
         return true;
     }
 
+    void MathUtils::SoftmaxF32(std::vector<float>& vec)
+    {
+        /* Fix for numerical stability and apply exp. */
+        auto start = vec.begin();
+        auto end = vec.end();
+
+        float maxValue = *std::max_element(start, end);
+        for (auto it = start; it != end; ++it) {
+            *it = std::exp((*it) - maxValue);
+        }
+
+        float sumExp = std::accumulate(start, end, 0.0f);
+
+        for (auto it = start; it != end; ++it) {
+            *it = (*it)/sumExp;
+        }
+    }
+
 } /* namespace math */
 } /* namespace app */
 } /* namespace arm */
diff --git a/source/application/main/include/Classifier.hpp b/source/application/main/include/Classifier.hpp
index 3ee3148..d899e8e 100644
--- a/source/application/main/include/Classifier.hpp
+++ b/source/application/main/include/Classifier.hpp
@@ -42,18 +42,33 @@
          *                             populated by this function.
          * @param[in]   labels         Labels vector to match classified classes.
          * @param[in]   topNCount      Number of top classifications to pick. Default is 1.
+         * @param[in]   useSoftmax     Whether Softmax normalisation should be applied to output. Default is false.
          * @return      true if successful, false otherwise.
          **/
+
         virtual bool GetClassificationResults(
             TfLiteTensor* outputTensor,
             std::vector<ClassificationResult>& vecResults,
-            const std::vector <std::string>& labels, uint32_t topNCount);
+            const std::vector <std::string>& labels, uint32_t topNCount,
+            bool use_softmax = false);
+
+        /**
+        * @brief       Populate the elements of the Classification Result object.
+        * @param[in]   topNSet        Ordered set of top 5 output class scores and labels.
+        * @param[out]  vecResults     A vector of classification results.
+        *                             populated by this function.
+        * @param[in]   labels         Labels vector to match classified classes.
+        **/
+
+        void SetVectorResults(
+            std::set<std::pair<float, uint32_t>>& topNSet,
+            std::vector<ClassificationResult>& vecResults,
+            const std::vector <std::string>& labels);
 
     private:
         /**
          * @brief       Utility function that gets the top N classification results from the
          *              output vector.
-         * @tparam T value type
          * @param[in]   tensor       Inference output tensor from an NN model.
          * @param[out]  vecResults   A vector of classification results
          *                           populated by this function.
@@ -61,8 +76,8 @@
          * @param[in]   labels       Labels vector to match classified classes.
          * @return      true if successful, false otherwise.
          **/
-        template<typename T>
-        bool GetTopNResults(TfLiteTensor* tensor,
+
+        bool GetTopNResults(const std::vector<float>& tensor,
                             std::vector<ClassificationResult>& vecResults,
                             uint32_t topNCount,
                             const std::vector <std::string>& labels);
diff --git a/source/application/main/include/PlatformMath.hpp b/source/application/main/include/PlatformMath.hpp
index 6804025..fdb51b2 100644
--- a/source/application/main/include/PlatformMath.hpp
+++ b/source/application/main/include/PlatformMath.hpp
@@ -161,7 +161,14 @@
                                                float* ptrDst,
                                                const uint32_t dstLen);
 
+        /**
+        * @brief       Scales output scores for an arbitrary number of classes so
+        *              that they sum to 1, allowing output to be expressed as a probability.
+        * @param[in]   vector Vector of floats modified in-place
+        */
+        static void SoftmaxF32(std::vector<float>& vec);
     };
+
 } /* namespace math */
 } /* namespace app */
 } /* namespace arm */
diff --git a/source/use_case/asr/include/AsrClassifier.hpp b/source/use_case/asr/include/AsrClassifier.hpp
index 2c97a39..67a200e 100644
--- a/source/use_case/asr/include/AsrClassifier.hpp
+++ b/source/use_case/asr/include/AsrClassifier.hpp
@@ -32,12 +32,13 @@
          *                             populated by this function.
          * @param[in]   labels         Labels vector to match classified classes
          * @param[in]   topNCount      Number of top classifications to pick.
+         * @param[in]   use_softmax    Whether softmax scaling should be applied to model output.
          * @return      true if successful, false otherwise.
          **/
         bool GetClassificationResults(
             TfLiteTensor* outputTensor,
             std::vector<ClassificationResult>& vecResults,
-            const std::vector <std::string>& labels, uint32_t topNCount) override;
+            const std::vector <std::string>& labels, uint32_t topNCount, bool use_softmax = false) override;
 
     private:
         /**
diff --git a/source/use_case/asr/src/AsrClassifier.cc b/source/use_case/asr/src/AsrClassifier.cc
index c18bd88..a715068 100644
--- a/source/use_case/asr/src/AsrClassifier.cc
+++ b/source/use_case/asr/src/AsrClassifier.cc
@@ -73,8 +73,9 @@
 bool arm::app::AsrClassifier::GetClassificationResults(
             TfLiteTensor* outputTensor,
             std::vector<ClassificationResult>& vecResults,
-            const std::vector <std::string>& labels, uint32_t topNCount)
+            const std::vector <std::string>& labels, uint32_t topNCount, bool use_softmax)
 {
+        UNUSED(use_softmax);
         vecResults.clear();
 
         constexpr int minTensorDims = static_cast<int>(
diff --git a/source/use_case/kws/include/DsCnnMfcc.hpp b/source/use_case/kws/include/DsCnnMfcc.hpp
deleted file mode 100644
index 3f681af..0000000
--- a/source/use_case/kws/include/DsCnnMfcc.hpp
+++ /dev/null
@@ -1,50 +0,0 @@
-/*
- * Copyright (c) 2021 Arm Limited. All rights reserved.
- * SPDX-License-Identifier: Apache-2.0
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-#ifndef KWS_DSCNN_MFCC_HPP
-#define KWS_DSCNN_MFCC_HPP
-
-#include "Mfcc.hpp"
-
-namespace arm {
-namespace app {
-namespace audio {
-
-    /* Class to provide DS-CNN specific MFCC calculation requirements. */
-    class DsCnnMFCC : public MFCC {
-
-    public:
-        static constexpr uint32_t  ms_defaultSamplingFreq = 16000;
-        static constexpr uint32_t  ms_defaultNumFbankBins =    40;
-        static constexpr uint32_t  ms_defaultMelLoFreq    =    20;
-        static constexpr uint32_t  ms_defaultMelHiFreq    =  4000;
-        static constexpr bool      ms_defaultUseHtkMethod =  true;
-
-        explicit DsCnnMFCC(const size_t numFeats, const size_t frameLen)
-            :  MFCC(MfccParams(
-                        ms_defaultSamplingFreq, ms_defaultNumFbankBins,
-                        ms_defaultMelLoFreq, ms_defaultMelHiFreq,
-                        numFeats, frameLen, ms_defaultUseHtkMethod))
-        {}
-        DsCnnMFCC()  = delete;
-        ~DsCnnMFCC() = default;
-    };
-
-} /* namespace audio */
-} /* namespace app */
-} /* namespace arm */
-
-#endif /* KWS_DSCNN_MFCC_HPP */
\ No newline at end of file
diff --git a/source/use_case/kws_asr/include/DsCnnMfcc.hpp b/source/use_case/kws/include/MicroNetKwsMfcc.hpp
similarity index 79%
rename from source/use_case/kws_asr/include/DsCnnMfcc.hpp
rename to source/use_case/kws/include/MicroNetKwsMfcc.hpp
index c97dd9d..b2565a3 100644
--- a/source/use_case/kws_asr/include/DsCnnMfcc.hpp
+++ b/source/use_case/kws/include/MicroNetKwsMfcc.hpp
@@ -14,8 +14,8 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-#ifndef KWS_ASR_DSCNN_MFCC_HPP
-#define KWS_ASR_DSCNN_MFCC_HPP
+#ifndef KWS_MICRONET_MFCC_HPP
+#define KWS_MICRONET_MFCC_HPP
 
 #include "Mfcc.hpp"
 
@@ -23,8 +23,8 @@
 namespace app {
 namespace audio {
 
-    /* Class to provide DS-CNN specific MFCC calculation requirements. */
-    class DsCnnMFCC : public MFCC {
+    /* Class to provide MicroNet specific MFCC calculation requirements. */
+    class MicroNetKwsMFCC : public MFCC {
 
     public:
         static constexpr uint32_t  ms_defaultSamplingFreq = 16000;
@@ -33,19 +33,18 @@
         static constexpr uint32_t  ms_defaultMelHiFreq    =  4000;
         static constexpr bool      ms_defaultUseHtkMethod =  true;
 
-
-        explicit DsCnnMFCC(const size_t numFeats, const size_t frameLen)
+        explicit MicroNetKwsMFCC(const size_t numFeats, const size_t frameLen)
             :  MFCC(MfccParams(
                         ms_defaultSamplingFreq, ms_defaultNumFbankBins,
                         ms_defaultMelLoFreq, ms_defaultMelHiFreq,
                         numFeats, frameLen, ms_defaultUseHtkMethod))
         {}
-        DsCnnMFCC()  = delete;
-        ~DsCnnMFCC() = default;
+        MicroNetKwsMFCC()  = delete;
+        ~MicroNetKwsMFCC() = default;
     };
 
 } /* namespace audio */
 } /* namespace app */
 } /* namespace arm */
 
-#endif /* KWS_ASR_DSCNN_MFCC_HPP */
+#endif /* KWS_MICRONET_MFCC_HPP */
\ No newline at end of file
diff --git a/source/use_case/kws/include/DsCnnModel.hpp b/source/use_case/kws/include/MicroNetKwsModel.hpp
similarity index 84%
rename from source/use_case/kws/include/DsCnnModel.hpp
rename to source/use_case/kws/include/MicroNetKwsModel.hpp
index a1a45cd..3259c45 100644
--- a/source/use_case/kws/include/DsCnnModel.hpp
+++ b/source/use_case/kws/include/MicroNetKwsModel.hpp
@@ -14,8 +14,8 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-#ifndef KWS_DSCNNMODEL_HPP
-#define KWS_DSCNNMODEL_HPP
+#ifndef KWS_MICRONETMODEL_HPP
+#define KWS_MICRONETMODEL_HPP
 
 #include "Model.hpp"
 
@@ -26,11 +26,11 @@
 namespace arm {
 namespace app {
 
-    class DsCnnModel : public Model {
+    class MicroNetKwsModel : public Model {
     public:
         /* Indices for the expected model - based on input and output tensor shapes */
-        static constexpr uint32_t ms_inputRowsIdx = 2;
-        static constexpr uint32_t ms_inputColsIdx = 3;
+        static constexpr uint32_t ms_inputRowsIdx = 1;
+        static constexpr uint32_t ms_inputColsIdx = 2;
         static constexpr uint32_t ms_outputRowsIdx = 2;
         static constexpr uint32_t ms_outputColsIdx = 3;
     
@@ -47,7 +47,7 @@
 
     private:
         /* Maximum number of individual operations that can be enlisted. */
-        static constexpr int ms_maxOpCnt = 8;
+        static constexpr int ms_maxOpCnt = 7;
 
         /* A mutable op resolver instance. */
         tflite::MicroMutableOpResolver<ms_maxOpCnt> m_opResolver;
@@ -56,4 +56,4 @@
 } /* namespace app */
 } /* namespace arm */
 
-#endif /* KWS_DSCNNMODEL_HPP */
+#endif /* KWS_MICRONETMODEL_HPP */
diff --git a/source/use_case/kws/src/MainLoop.cc b/source/use_case/kws/src/MainLoop.cc
index c683e71..bde246b 100644
--- a/source/use_case/kws/src/MainLoop.cc
+++ b/source/use_case/kws/src/MainLoop.cc
@@ -16,7 +16,7 @@
  */
 #include "InputFiles.hpp"           /* For input audio clips. */
 #include "Classifier.hpp"           /* Classifier. */
-#include "DsCnnModel.hpp"           /* Model class for running inference. */
+#include "MicroNetKwsModel.hpp"     /* Model class for running inference. */
 #include "hal.h"                    /* Brings in platform definitions. */
 #include "Labels.hpp"               /* For label strings. */
 #include "UseCaseHandler.hpp"       /* Handlers for different user options. */
@@ -49,7 +49,7 @@
 
 void main_loop(hal_platform& platform)
 {
-    arm::app::DsCnnModel model;  /* Model wrapper object. */
+    arm::app::MicroNetKwsModel model;  /* Model wrapper object. */
 
     /* Load the model. */
     if (!model.Init()) {
diff --git a/source/use_case/kws/src/DsCnnModel.cc b/source/use_case/kws/src/MicroNetKwsModel.cc
similarity index 83%
rename from source/use_case/kws/src/DsCnnModel.cc
rename to source/use_case/kws/src/MicroNetKwsModel.cc
index 4edfc04..48a9b8c 100644
--- a/source/use_case/kws/src/DsCnnModel.cc
+++ b/source/use_case/kws/src/MicroNetKwsModel.cc
@@ -14,16 +14,16 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-#include "DsCnnModel.hpp"
+#include "MicroNetKwsModel.hpp"
 
 #include "hal.h"
 
-const tflite::MicroOpResolver& arm::app::DsCnnModel::GetOpResolver()
+const tflite::MicroOpResolver& arm::app::MicroNetKwsModel::GetOpResolver()
 {
     return this->m_opResolver;
 }
 
-bool arm::app::DsCnnModel::EnlistOperations()
+bool arm::app::MicroNetKwsModel::EnlistOperations()
 {
     this->m_opResolver.AddReshape();
     this->m_opResolver.AddAveragePool2D();
@@ -31,7 +31,6 @@
     this->m_opResolver.AddDepthwiseConv2D();
     this->m_opResolver.AddFullyConnected();
     this->m_opResolver.AddRelu();
-    this->m_opResolver.AddSoftmax();
 
 #if defined(ARM_NPU)
     if (kTfLiteOk == this->m_opResolver.AddEthosU()) {
@@ -46,13 +45,13 @@
 }
 
 extern uint8_t* GetModelPointer();
-const uint8_t* arm::app::DsCnnModel::ModelPointer()
+const uint8_t* arm::app::MicroNetKwsModel::ModelPointer()
 {
     return GetModelPointer();
 }
 
 extern size_t GetModelLen();
-size_t arm::app::DsCnnModel::ModelSize()
+size_t arm::app::MicroNetKwsModel::ModelSize()
 {
     return GetModelLen();
 }
\ No newline at end of file
diff --git a/source/use_case/kws/src/UseCaseHandler.cc b/source/use_case/kws/src/UseCaseHandler.cc
index 3d95753..8085af7 100644
--- a/source/use_case/kws/src/UseCaseHandler.cc
+++ b/source/use_case/kws/src/UseCaseHandler.cc
@@ -18,9 +18,9 @@
 
 #include "InputFiles.hpp"
 #include "Classifier.hpp"
-#include "DsCnnModel.hpp"
+#include "MicroNetKwsModel.hpp"
 #include "hal.h"
-#include "DsCnnMfcc.hpp"
+#include "MicroNetKwsMfcc.hpp"
 #include "AudioUtils.hpp"
 #include "UseCaseCommonUtils.hpp"
 #include "KwsResult.hpp"
@@ -59,7 +59,7 @@
      * @return          Function to be called providing audio sample and sliding window index.
      */
     static std::function<void (std::vector<int16_t>&, int, bool, size_t)>
-            GetFeatureCalculator(audio::DsCnnMFCC&  mfcc,
+            GetFeatureCalculator(audio::MicroNetKwsMFCC&  mfcc,
                                  TfLiteTensor*      inputTensor,
                                  size_t             cacheSize);
 
@@ -72,8 +72,8 @@
         constexpr uint32_t dataPsnTxtInfStartX = 20;
         constexpr uint32_t dataPsnTxtInfStartY = 40;
         constexpr int minTensorDims = static_cast<int>(
-            (arm::app::DsCnnModel::ms_inputRowsIdx > arm::app::DsCnnModel::ms_inputColsIdx)?
-             arm::app::DsCnnModel::ms_inputRowsIdx : arm::app::DsCnnModel::ms_inputColsIdx);
+            (arm::app::MicroNetKwsModel::ms_inputRowsIdx > arm::app::MicroNetKwsModel::ms_inputColsIdx)?
+             arm::app::MicroNetKwsModel::ms_inputRowsIdx : arm::app::MicroNetKwsModel::ms_inputColsIdx);
 
         auto& model = ctx.Get<Model&>("model");
 
@@ -105,10 +105,10 @@
         }
 
         TfLiteIntArray* inputShape = model.GetInputShape(0);
-        const uint32_t kNumCols = inputShape->data[arm::app::DsCnnModel::ms_inputColsIdx];
-        const uint32_t kNumRows = inputShape->data[arm::app::DsCnnModel::ms_inputRowsIdx];
+        const uint32_t kNumCols = inputShape->data[arm::app::MicroNetKwsModel::ms_inputColsIdx];
+        const uint32_t kNumRows = inputShape->data[arm::app::MicroNetKwsModel::ms_inputRowsIdx];
 
-        audio::DsCnnMFCC mfcc = audio::DsCnnMFCC(kNumCols, frameLength);
+        audio::MicroNetKwsMFCC mfcc = audio::MicroNetKwsMFCC(kNumCols, frameLength);
         mfcc.Init();
 
         /* Deduce the data length required for 1 inference from the network parameters. */
@@ -132,7 +132,7 @@
 
         /* We expect to be sampling 1 second worth of data at a time.
          * NOTE: This is only used for time stamp calculation. */
-        const float secondsPerSample = 1.0/audio::DsCnnMFCC::ms_defaultSamplingFreq;
+        const float secondsPerSample = 1.0/audio::MicroNetKwsMFCC::ms_defaultSamplingFreq;
 
         do {
             platform.data_psn->clear(COLOR_BLACK);
@@ -208,7 +208,7 @@
                 std::vector<ClassificationResult> classificationResult;
                 auto& classifier = ctx.Get<KwsClassifier&>("classifier");
                 classifier.GetClassificationResults(outputTensor, classificationResult,
-                                                    ctx.Get<std::vector<std::string>&>("labels"), 1);
+                                                    ctx.Get<std::vector<std::string>&>("labels"), 1, true);
 
                 results.emplace_back(kws::KwsResult(classificationResult,
                     audioDataSlider.Index() * secondsPerSample * audioDataStride,
@@ -240,7 +240,6 @@
         return true;
     }
 
-    
     static bool PresentInferenceResult(hal_platform& platform,
                                        const std::vector<arm::app::kws::KwsResult>& results)
     {
@@ -259,7 +258,6 @@
 
             std::string topKeyword{"<none>"};
             float score = 0.f;
-
             if (!results[i].m_resultVec.empty()) {
                 topKeyword = results[i].m_resultVec[0].m_label;
                 score = results[i].m_resultVec[0].m_normalisedVal;
@@ -366,7 +364,7 @@
 
 
     static std::function<void (std::vector<int16_t>&, int, bool, size_t)>
-    GetFeatureCalculator(audio::DsCnnMFCC& mfcc, TfLiteTensor* inputTensor, size_t cacheSize)
+    GetFeatureCalculator(audio::MicroNetKwsMFCC& mfcc, TfLiteTensor* inputTensor, size_t cacheSize)
     {
         std::function<void (std::vector<int16_t>&, size_t, bool, size_t)> mfccFeatureCalc;
 
diff --git a/source/use_case/kws/usecase.cmake b/source/use_case/kws/usecase.cmake
index 34e39e4..9f3736e 100644
--- a/source/use_case/kws/usecase.cmake
+++ b/source/use_case/kws/usecase.cmake
@@ -20,7 +20,7 @@
     PATH_OR_FILE)
 
 USER_OPTION(${use_case}_LABELS_TXT_FILE "Labels' txt file for the chosen model."
-    ${CMAKE_CURRENT_SOURCE_DIR}/resources/${use_case}/labels/ds_cnn_labels.txt
+    ${CMAKE_CURRENT_SOURCE_DIR}/resources/${use_case}/labels/micronet_kws_labels.txt
     FILEPATH)
 
 USER_OPTION(${use_case}_AUDIO_RATE "Specify the target sampling rate. Default is 16000."
@@ -48,7 +48,7 @@
     STRING)
 
 USER_OPTION(${use_case}_MODEL_SCORE_THRESHOLD "Specify the score threshold [0.0, 1.0) that must be applied to the inference results for a label to be deemed valid."
-    0.9
+    0.7
     STRING)
 
 # Generate input files
@@ -73,10 +73,11 @@
     0x00100000
     STRING)
 
+
 if (ETHOS_U_NPU_ENABLED)
-    set(DEFAULT_MODEL_PATH      ${DEFAULT_MODEL_DIR}/ds_cnn_clustered_int8_vela_${ETHOS_U_NPU_CONFIG_ID}.tflite)
+    set(DEFAULT_MODEL_PATH      ${DEFAULT_MODEL_DIR}/kws_micronet_m_vela_${ETHOS_U_NPU_CONFIG_ID}.tflite)
 else()
-    set(DEFAULT_MODEL_PATH      ${DEFAULT_MODEL_DIR}/ds_cnn_clustered_int8.tflite)
+    set(DEFAULT_MODEL_PATH      ${DEFAULT_MODEL_DIR}/kws_micronet_m.tflite)
 endif()
 
 set(EXTRA_MODEL_CODE
diff --git a/source/use_case/kws_asr/include/AsrClassifier.hpp b/source/use_case/kws_asr/include/AsrClassifier.hpp
index 7dbb6e9..6ab9685 100644
--- a/source/use_case/kws_asr/include/AsrClassifier.hpp
+++ b/source/use_case/kws_asr/include/AsrClassifier.hpp
@@ -32,12 +32,14 @@
          *                             populated by this function.
          * @param[in]   labels         Labels vector to match classified classes
          * @param[in]   topNCount      Number of top classifications to pick.
+         * @param[in]   use_softmax    Whether softmax scaling should be applied to model output.
          * @return      true if successful, false otherwise.
          **/
         bool GetClassificationResults(
                 TfLiteTensor* outputTensor,
                 std::vector<ClassificationResult>& vecResults,
-                const std::vector <std::string>& labels, uint32_t topNCount) override;
+                const std::vector <std::string>& labels, uint32_t topNCount,
+                bool use_softmax = false) override;
 
     private:
 
diff --git a/source/use_case/kws_asr/include/DsCnnMfcc.hpp b/source/use_case/kws_asr/include/MicroNetKwsMfcc.hpp
similarity index 79%
copy from source/use_case/kws_asr/include/DsCnnMfcc.hpp
copy to source/use_case/kws_asr/include/MicroNetKwsMfcc.hpp
index c97dd9d..43bd390 100644
--- a/source/use_case/kws_asr/include/DsCnnMfcc.hpp
+++ b/source/use_case/kws_asr/include/MicroNetKwsMfcc.hpp
@@ -14,8 +14,8 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-#ifndef KWS_ASR_DSCNN_MFCC_HPP
-#define KWS_ASR_DSCNN_MFCC_HPP
+#ifndef KWS_ASR_MICRONET_MFCC_HPP
+#define KWS_ASR_MICRONET_MFCC_HPP
 
 #include "Mfcc.hpp"
 
@@ -23,8 +23,8 @@
 namespace app {
 namespace audio {
 
-    /* Class to provide DS-CNN specific MFCC calculation requirements. */
-    class DsCnnMFCC : public MFCC {
+    /* Class to provide MicroNet specific MFCC calculation requirements. */
+    class MicroNetMFCC : public MFCC {
 
     public:
         static constexpr uint32_t  ms_defaultSamplingFreq = 16000;
@@ -34,18 +34,18 @@
         static constexpr bool      ms_defaultUseHtkMethod =  true;
 
 
-        explicit DsCnnMFCC(const size_t numFeats, const size_t frameLen)
+        explicit MicroNetMFCC(const size_t numFeats, const size_t frameLen)
             :  MFCC(MfccParams(
                         ms_defaultSamplingFreq, ms_defaultNumFbankBins,
                         ms_defaultMelLoFreq, ms_defaultMelHiFreq,
                         numFeats, frameLen, ms_defaultUseHtkMethod))
         {}
-        DsCnnMFCC()  = delete;
-        ~DsCnnMFCC() = default;
+        MicroNetMFCC()  = delete;
+        ~MicroNetMFCC() = default;
     };
 
 } /* namespace audio */
 } /* namespace app */
 } /* namespace arm */
 
-#endif /* KWS_ASR_DSCNN_MFCC_HPP */
+#endif /* KWS_ASR_MICRONET_MFCC_HPP */
diff --git a/source/use_case/kws_asr/include/DsCnnModel.hpp b/source/use_case/kws_asr/include/MicroNetKwsModel.hpp
similarity index 85%
rename from source/use_case/kws_asr/include/DsCnnModel.hpp
rename to source/use_case/kws_asr/include/MicroNetKwsModel.hpp
index 92d96b9..22cf916 100644
--- a/source/use_case/kws_asr/include/DsCnnModel.hpp
+++ b/source/use_case/kws_asr/include/MicroNetKwsModel.hpp
@@ -14,8 +14,8 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-#ifndef KWS_ASR_DSCNNMODEL_HPP
-#define KWS_ASR_DSCNNMODEL_HPP
+#ifndef KWS_ASR_MICRONETMODEL_HPP
+#define KWS_ASR_MICRONETMODEL_HPP
 
 #include "Model.hpp"
 
@@ -33,12 +33,11 @@
 
 namespace arm {
 namespace app {
-
-    class DsCnnModel : public Model {
+    class MicroNetKwsModel : public Model {
     public:
         /* Indices for the expected model - based on input and output tensor shapes */
-        static constexpr uint32_t ms_inputRowsIdx = 2;
-        static constexpr uint32_t ms_inputColsIdx = 3;
+        static constexpr uint32_t ms_inputRowsIdx = 1;
+        static constexpr uint32_t ms_inputColsIdx = 2;
         static constexpr uint32_t ms_outputRowsIdx = 2;
         static constexpr uint32_t ms_outputColsIdx = 3;
 
@@ -55,7 +54,7 @@
 
     private:
         /* Maximum number of individual operations that can be enlisted. */
-        static constexpr int ms_maxOpCnt = 10;
+        static constexpr int ms_maxOpCnt = 7;
 
         /* A mutable op resolver instance. */
         tflite::MicroMutableOpResolver<ms_maxOpCnt> m_opResolver;
@@ -64,4 +63,4 @@
 } /* namespace app */
 } /* namespace arm */
 
-#endif /* KWS_DSCNNMODEL_HPP */
+#endif /* KWS_ASR_MICRONETMODEL_HPP */
diff --git a/source/use_case/kws_asr/src/AsrClassifier.cc b/source/use_case/kws_asr/src/AsrClassifier.cc
index 57d5058..3f9cd7b 100644
--- a/source/use_case/kws_asr/src/AsrClassifier.cc
+++ b/source/use_case/kws_asr/src/AsrClassifier.cc
@@ -73,8 +73,9 @@
 bool arm::app::AsrClassifier::GetClassificationResults(
             TfLiteTensor* outputTensor,
             std::vector<ClassificationResult>& vecResults,
-            const std::vector <std::string>& labels, uint32_t topNCount)
+            const std::vector <std::string>& labels, uint32_t topNCount, bool use_softmax)
 {
+        UNUSED(use_softmax);
         vecResults.clear();
 
         constexpr int minTensorDims = static_cast<int>(
diff --git a/source/use_case/kws_asr/src/MainLoop.cc b/source/use_case/kws_asr/src/MainLoop.cc
index d5a2c2b..30cb084 100644
--- a/source/use_case/kws_asr/src/MainLoop.cc
+++ b/source/use_case/kws_asr/src/MainLoop.cc
@@ -16,11 +16,11 @@
  */
 #include "hal.h"                    /* Brings in platform definitions. */
 #include "InputFiles.hpp"           /* For input images. */
-#include "Labels_dscnn.hpp"         /* For DS-CNN label strings. */
+#include "Labels_micronetkws.hpp"   /* For MicroNetKws label strings. */
 #include "Labels_wav2letter.hpp"    /* For Wav2Letter label strings. */
 #include "Classifier.hpp"           /* KWS classifier. */
 #include "AsrClassifier.hpp"        /* ASR classifier. */
-#include "DsCnnModel.hpp"           /* KWS model class for running inference. */
+#include "MicroNetKwsModel.hpp"     /* KWS model class for running inference. */
 #include "Wav2LetterModel.hpp"      /* ASR model class for running inference. */
 #include "UseCaseCommonUtils.hpp"   /* Utils functions. */
 #include "UseCaseHandler.hpp"       /* Handlers for different user options. */
@@ -69,7 +69,7 @@
 void main_loop(hal_platform& platform)
 {
     /* Model wrapper objects. */
-    arm::app::DsCnnModel kwsModel;
+    arm::app::MicroNetKwsModel kwsModel;
     arm::app::Wav2LetterModel asrModel;
 
     /* Load the models. */
@@ -81,7 +81,7 @@
     /* Initialise the asr model using the same allocator from KWS
      * to re-use the tensor arena. */
     if (!asrModel.Init(kwsModel.GetAllocator())) {
-        printf_err("Failed to initalise ASR model\n");
+        printf_err("Failed to initialise ASR model\n");
         return;
     }
 
@@ -137,7 +137,7 @@
     caseContext.Set<const std::vector <std::string>&>("kwslabels", kwsLabels);
 
     /* Index of the kws outputs we trigger ASR on. */
-    caseContext.Set<uint32_t>("keywordindex", 2);
+    caseContext.Set<uint32_t>("keywordindex", 9 );
 
     /* Loop. */
     bool executionSuccessful = true;
diff --git a/source/use_case/kws_asr/src/DsCnnModel.cc b/source/use_case/kws_asr/src/MicroNetKwsModel.cc
similarity index 81%
rename from source/use_case/kws_asr/src/DsCnnModel.cc
rename to source/use_case/kws_asr/src/MicroNetKwsModel.cc
index 71d4ceb..4b44580 100644
--- a/source/use_case/kws_asr/src/DsCnnModel.cc
+++ b/source/use_case/kws_asr/src/MicroNetKwsModel.cc
@@ -14,7 +14,7 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-#include "DsCnnModel.hpp"
+#include "MicroNetKwsModel.hpp"
 
 #include "hal.h"
 
@@ -27,21 +27,18 @@
 } /* namespace app */
 } /* namespace arm */
 
-const tflite::MicroOpResolver& arm::app::DsCnnModel::GetOpResolver()
+const tflite::MicroOpResolver& arm::app::MicroNetKwsModel::GetOpResolver()
 {
     return this->m_opResolver;
 }
 
-bool arm::app::DsCnnModel::EnlistOperations()
+bool arm::app::MicroNetKwsModel::EnlistOperations()
 {
     this->m_opResolver.AddAveragePool2D();
     this->m_opResolver.AddConv2D();
     this->m_opResolver.AddDepthwiseConv2D();
     this->m_opResolver.AddFullyConnected();
     this->m_opResolver.AddRelu();
-    this->m_opResolver.AddSoftmax();
-    this->m_opResolver.AddQuantize();
-    this->m_opResolver.AddDequantize();
     this->m_opResolver.AddReshape();
 
 #if defined(ARM_NPU)
@@ -56,12 +53,12 @@
     return true;
 }
 
-const uint8_t* arm::app::DsCnnModel::ModelPointer()
+const uint8_t* arm::app::MicroNetKwsModel::ModelPointer()
 {
     return arm::app::kws::GetModelPointer();
 }
 
-size_t arm::app::DsCnnModel::ModelSize()
+size_t arm::app::MicroNetKwsModel::ModelSize()
 {
     return arm::app::kws::GetModelLen();
 }
\ No newline at end of file
diff --git a/source/use_case/kws_asr/src/UseCaseHandler.cc b/source/use_case/kws_asr/src/UseCaseHandler.cc
index 1d88ba1..c67be22 100644
--- a/source/use_case/kws_asr/src/UseCaseHandler.cc
+++ b/source/use_case/kws_asr/src/UseCaseHandler.cc
@@ -20,8 +20,8 @@
 #include "InputFiles.hpp"
 #include "AudioUtils.hpp"
 #include "UseCaseCommonUtils.hpp"
-#include "DsCnnModel.hpp"
-#include "DsCnnMfcc.hpp"
+#include "MicroNetKwsModel.hpp"
+#include "MicroNetKwsMfcc.hpp"
 #include "Classifier.hpp"
 #include "KwsResult.hpp"
 #include "Wav2LetterMfcc.hpp"
@@ -77,12 +77,12 @@
      *
      * @param[in]           mfcc            MFCC feature calculator.
      * @param[in,out]       inputTensor     Input tensor pointer to store calculated features.
-     * @param[in]            cacheSize      Size of the feture vectors cache (number of feature vectors).
+     * @param[in]           cacheSize       Size of the feature vectors cache (number of feature vectors).
      *
      * @return function     function to be called providing audio sample and sliding window index.
      **/
     static std::function<void (std::vector<int16_t>&, int, bool, size_t)>
-    GetFeatureCalculator(audio::DsCnnMFCC&  mfcc,
+    GetFeatureCalculator(audio::MicroNetMFCC&  mfcc,
                          TfLiteTensor*      inputTensor,
                          size_t             cacheSize);
 
@@ -98,8 +98,8 @@
         constexpr uint32_t dataPsnTxtInfStartY = 40;
 
         constexpr int minTensorDims = static_cast<int>(
-            (arm::app::DsCnnModel::ms_inputRowsIdx > arm::app::DsCnnModel::ms_inputColsIdx)?
-             arm::app::DsCnnModel::ms_inputRowsIdx : arm::app::DsCnnModel::ms_inputColsIdx);
+            (arm::app::MicroNetKwsModel::ms_inputRowsIdx > arm::app::MicroNetKwsModel::ms_inputColsIdx)?
+             arm::app::MicroNetKwsModel::ms_inputRowsIdx : arm::app::MicroNetKwsModel::ms_inputColsIdx);
 
         KWSOutput output;
 
@@ -128,7 +128,7 @@
         const uint32_t kwsNumMfccFeats = ctx.Get<uint32_t>("kwsNumMfcc");
         const uint32_t kwsNumAudioWindows = ctx.Get<uint32_t>("kwsNumAudioWins");
 
-        audio::DsCnnMFCC kwsMfcc = audio::DsCnnMFCC(kwsNumMfccFeats, kwsFrameLength);
+        audio::MicroNetMFCC kwsMfcc = audio::MicroNetMFCC(kwsNumMfccFeats, kwsFrameLength);
         kwsMfcc.Init();
 
         /* Deduce the data length required for 1 KWS inference from the network parameters. */
@@ -152,7 +152,7 @@
 
         /* We expect to be sampling 1 second worth of data at a time
          * NOTE: This is only used for time stamp calculation. */
-        const float kwsAudioParamsSecondsPerSample = 1.0/audio::DsCnnMFCC::ms_defaultSamplingFreq;
+        const float kwsAudioParamsSecondsPerSample = 1.0/audio::MicroNetMFCC::ms_defaultSamplingFreq;
 
         auto currentIndex = ctx.Get<uint32_t>("clipIndex");
 
@@ -230,7 +230,7 @@
 
             kwsClassifier.GetClassificationResults(
                             kwsOutputTensor, kwsClassificationResult,
-                            ctx.Get<std::vector<std::string>&>("kwslabels"), 1);
+                            ctx.Get<std::vector<std::string>&>("kwslabels"), 1, true);
 
             kwsResults.emplace_back(
                 kws::KwsResult(
@@ -604,7 +604,7 @@
 
 
     static std::function<void (std::vector<int16_t>&, int, bool, size_t)>
-    GetFeatureCalculator(audio::DsCnnMFCC& mfcc, TfLiteTensor* inputTensor, size_t cacheSize)
+    GetFeatureCalculator(audio::MicroNetMFCC& mfcc, TfLiteTensor* inputTensor, size_t cacheSize)
     {
         std::function<void (std::vector<int16_t>&, size_t, bool, size_t)> mfccFeatureCalc;
 
diff --git a/source/use_case/kws_asr/usecase.cmake b/source/use_case/kws_asr/usecase.cmake
index d8629b6..b3fe020 100644
--- a/source/use_case/kws_asr/usecase.cmake
+++ b/source/use_case/kws_asr/usecase.cmake
@@ -45,7 +45,7 @@
 
 # Generate kws labels file:
 USER_OPTION(${use_case}_LABELS_TXT_FILE_KWS "Labels' txt file for the chosen model."
-    ${CMAKE_CURRENT_SOURCE_DIR}/resources/${use_case}/labels/ds_cnn_labels.txt
+    ${CMAKE_CURRENT_SOURCE_DIR}/resources/${use_case}/labels/micronet_kws_labels.txt
     FILEPATH)
 
 # Generate asr labels file:
@@ -67,10 +67,10 @@
     STRING)
 
 if (ETHOS_U_NPU_ENABLED)
-    set(DEFAULT_MODEL_PATH_KWS      ${DEFAULT_MODEL_DIR}/ds_cnn_clustered_int8_vela_${ETHOS_U_NPU_CONFIG_ID}.tflite)
+    set(DEFAULT_MODEL_PATH_KWS      ${DEFAULT_MODEL_DIR}/kws_micronet_m_vela_${ETHOS_U_NPU_CONFIG_ID}.tflite)
     set(DEFAULT_MODEL_PATH_ASR      ${DEFAULT_MODEL_DIR}/wav2letter_pruned_int8_vela_${ETHOS_U_NPU_CONFIG_ID}.tflite)
 else()
-    set(DEFAULT_MODEL_PATH_KWS      ${DEFAULT_MODEL_DIR}/ds_cnn_clustered_int8.tflite)
+    set(DEFAULT_MODEL_PATH_KWS      ${DEFAULT_MODEL_DIR}/kws_micronet_m.tflite)
     set(DEFAULT_MODEL_PATH_ASR      ${DEFAULT_MODEL_DIR}/wav2letter_pruned_int8.tflite)
 endif()
 
@@ -134,7 +134,7 @@
         INPUT           "${${use_case}_LABELS_TXT_FILE_KWS}"
         DESTINATION_SRC ${SRC_GEN_DIR}
         DESTINATION_HDR ${INC_GEN_DIR}
-        OUTPUT_FILENAME "Labels_dscnn"
+        OUTPUT_FILENAME "Labels_micronetkws"
         NAMESPACE       "arm" "app" "kws"
 )
 
diff --git a/tests/common/ClassifierTests.cc b/tests/common/ClassifierTests.cc
index d950304..693f744 100644
--- a/tests/common/ClassifierTests.cc
+++ b/tests/common/ClassifierTests.cc
@@ -35,7 +35,7 @@
     }
 
     arm::app::Classifier classifier;
-    REQUIRE(classifier.GetClassificationResults(outputTensor, resultVec, labels, 5));
+    REQUIRE(classifier.GetClassificationResults(outputTensor, resultVec, labels, 5, true));
     REQUIRE(5 == resultVec.size());
 
     for (size_t i = 0; i < resultVec.size(); ++i) {
@@ -50,7 +50,7 @@
         TfLiteTensor* outputTens = nullptr;
         std::vector <arm::app::ClassificationResult> resultVec;
         arm::app::Classifier classifier;
-        REQUIRE(!classifier.GetClassificationResults(outputTens, resultVec, {}, 5));
+        REQUIRE(!classifier.GetClassificationResults(outputTens, resultVec, {}, 5, true));
     }
 
     SECTION("Test classification results")
diff --git a/tests/use_case/kws/InferenceTestDSCNN.cc b/tests/use_case/kws/InferenceTestMicroNetKws.cc
similarity index 91%
rename from tests/use_case/kws/InferenceTestDSCNN.cc
rename to tests/use_case/kws/InferenceTestMicroNetKws.cc
index 8918073..e6e7753 100644
--- a/tests/use_case/kws/InferenceTestDSCNN.cc
+++ b/tests/use_case/kws/InferenceTestMicroNetKws.cc
@@ -14,7 +14,7 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-#include "DsCnnModel.hpp"
+#include "MicroNetKwsModel.hpp"
 #include "hal.h"
 #include "TestData_kws.hpp"
 #include "TensorFlowLiteMicro.hpp"
@@ -74,9 +74,9 @@
     }
 }
 
-TEST_CASE("Running random inference with TensorFlow Lite Micro and DsCnnModel Int8", "[DS_CNN]")
+TEST_CASE("Running random inference with TensorFlow Lite Micro and MicroNetKwsModel Int8", "[MicroNetKws]")
 {
-    arm::app::DsCnnModel model{};
+    arm::app::MicroNetKwsModel model{};
 
     REQUIRE_FALSE(model.IsInited());
     REQUIRE(model.Init());
@@ -85,7 +85,7 @@
     REQUIRE(RunInferenceRandom(model));
 }
 
-TEST_CASE("Running inference with TensorFlow Lite Micro and DsCnnModel Uint8", "[DS_CNN]")
+TEST_CASE("Running inference with TensorFlow Lite Micro and MicroNetKwsModel int8", "[MicroNetKws]")
 {
     REQUIRE(NUMBER_OF_IFM_FILES == NUMBER_OF_OFM_FILES);
     for (uint32_t i = 0 ; i < NUMBER_OF_IFM_FILES; ++i) {
@@ -94,7 +94,7 @@
 
         DYNAMIC_SECTION("Executing inference with re-init")
         {
-            arm::app::DsCnnModel model{};
+            arm::app::MicroNetKwsModel model{};
 
             REQUIRE_FALSE(model.IsInited());
             REQUIRE(model.Init());
diff --git a/tests/use_case/kws/KWSHandlerTest.cc b/tests/use_case/kws/KWSHandlerTest.cc
index 50e5a83..a7e75fb 100644
--- a/tests/use_case/kws/KWSHandlerTest.cc
+++ b/tests/use_case/kws/KWSHandlerTest.cc
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 #include <catch.hpp>
-#include "DsCnnModel.hpp"
+#include "MicroNetKwsModel.hpp"
 #include "hal.h"
 
 #include "KwsResult.hpp"
@@ -27,7 +27,7 @@
 TEST_CASE("Model info")
 {
     /* Model wrapper object. */
-    arm::app::DsCnnModel model;
+    arm::app::MicroNetKwsModel model;
 
     /* Load the model. */
     REQUIRE(model.Init());
@@ -53,7 +53,7 @@
     hal_platform_init(&platform);
 
     /* Model wrapper object. */
-    arm::app::DsCnnModel model;
+    arm::app::MicroNetKwsModel model;
 
     /* Load the model. */
     REQUIRE(model.Init());
@@ -65,8 +65,8 @@
     caseContext.Set<arm::app::Profiler&>("profiler", profiler);
     caseContext.Set<hal_platform&>("platform", platform);
     caseContext.Set<arm::app::Model&>("model", model);
-    caseContext.Set<int>("frameLength", g_FrameLength);  /* 640 sample length for DSCNN. */
-    caseContext.Set<int>("frameStride", g_FrameStride);  /* 320 sample stride for DSCNN. */
+    caseContext.Set<int>("frameLength", g_FrameLength);  /* 640 sample length for MicroNetKws. */
+    caseContext.Set<int>("frameStride", g_FrameStride);  /* 320 sample stride for MicroNetKws. */
     caseContext.Set<float>("scoreThreshold", 0.5);       /* Normalised score threshold. */
 
     arm::app::Classifier classifier;                     /* classifier wrapper object. */
@@ -97,25 +97,25 @@
     SECTION("Index = 0, short clip down")
     {
         /* Result: down. */
-        checker(0, {5});
+        checker(0, {0});
     }
 
     SECTION("Index = 1, long clip right->left->up")
     {
         /* Result: right->right->left->up->up. */
-        checker(1, {7, 1, 6, 4, 4});
+        checker(1, {6, 6, 2, 8, 8});
     }
 
     SECTION("Index = 2, short clip yes")
     {
         /* Result: yes. */
-        checker(2, {2});
+        checker(2, {9});
     }
 
     SECTION("Index = 3, long clip yes->no->go->stop")
     {
-        /* Result: yes->go->no->go->go->go->stop. */
-        checker(3, {2, 11, 3, 11, 11, 11, 10});
+        /* Result: yes->no->no->go->go->stop->stop. */
+        checker(3, {9, 3, 3, 1, 1, 7, 7});
     }
 }
 
@@ -132,7 +132,7 @@
     hal_platform_init(&platform);
 
     /* Model wrapper object. */
-    arm::app::DsCnnModel model;
+    arm::app::MicroNetKwsModel model;
 
     /* Load the model. */
     REQUIRE(model.Init());
@@ -145,9 +145,9 @@
     caseContext.Set<hal_platform&>("platform", platform);
     caseContext.Set<arm::app::Model&>("model", model);
     caseContext.Set<uint32_t>("clipIndex", 0);
-    caseContext.Set<int>("frameLength", g_FrameLength);  /* 640 sample length for DSCNN. */
-    caseContext.Set<int>("frameStride", g_FrameStride);  /* 320 sample stride for DSCNN. */
-    caseContext.Set<float>("scoreThreshold", 0.9);       /* Normalised score threshold. */
+    caseContext.Set<int>("frameLength", g_FrameLength);  /* 640 sample length for MicroNet. */
+    caseContext.Set<int>("frameStride", g_FrameStride);  /* 320 sample stride for MicroNet. */
+    caseContext.Set<float>("scoreThreshold", 0.7);       /* Normalised score threshold. */
     arm::app::Classifier classifier;                     /* classifier wrapper object. */
     caseContext.Set<arm::app::Classifier&>("classifier", classifier);
 
@@ -170,7 +170,7 @@
     hal_platform_init(&platform);
 
     /* Model wrapper object. */
-    arm::app::DsCnnModel model;
+    arm::app::MicroNetKwsModel model;
 
     /* Load the model. */
     REQUIRE(model.Init());
diff --git a/tests/use_case/kws/MfccTests.cc b/tests/use_case/kws/MfccTests.cc
index 407861f..1d30ef4 100644
--- a/tests/use_case/kws/MfccTests.cc
+++ b/tests/use_case/kws/MfccTests.cc
@@ -14,7 +14,7 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-#include "DsCnnMfcc.hpp"
+#include "MicroNetKwsMfcc.hpp"
 
 #include <algorithm>
 #include <catch.hpp>
@@ -93,13 +93,13 @@
     -22.67135, -0.61615, 2.07233, 0.58137, 1.01655, 0.85816, 0.46039, 0.03393, 1.16511, 0.0072,
 };
 
-arm::app::audio::DsCnnMFCC GetMFCCInstance() {
-    const int sampFreq = arm::app::audio::DsCnnMFCC::ms_defaultSamplingFreq;
+arm::app::audio::MicroNetKwsMFCC GetMFCCInstance() {
+    const int sampFreq = arm::app::audio::MicroNetKwsMFCC::ms_defaultSamplingFreq;
     const int frameLenMs = 40;
     const int frameLenSamples = sampFreq * frameLenMs * 0.001;
     const int numMfccFeats = 10;
 
-   return arm::app::audio::DsCnnMFCC(numMfccFeats, frameLenSamples);
+   return arm::app::audio::MicroNetKwsMFCC(numMfccFeats, frameLenSamples);
 }
 
 template <class T>
diff --git a/tests/use_case/kws_asr/InferenceTestDSCNN.cc b/tests/use_case/kws_asr/InferenceTestMicroNetKws.cc
similarity index 90%
rename from tests/use_case/kws_asr/InferenceTestDSCNN.cc
rename to tests/use_case/kws_asr/InferenceTestMicroNetKws.cc
index ad1731b..fd379b6 100644
--- a/tests/use_case/kws_asr/InferenceTestDSCNN.cc
+++ b/tests/use_case/kws_asr/InferenceTestMicroNetKws.cc
@@ -14,7 +14,7 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-#include "DsCnnModel.hpp"
+#include "MicroNetKwsModel.hpp"
 #include "hal.h"
 #include "TestData_kws.hpp"
 #include "TensorFlowLiteMicro.hpp"
@@ -72,8 +72,8 @@
     }
 }
 
-TEST_CASE("Running random inference with Tflu and DsCnnModel Int8", "[DS_CNN]") {
-    arm::app::DsCnnModel model{};
+TEST_CASE("Running random inference with Tflu and MicroNetKwsModel Int8", "[MicroNetKws]") {
+    arm::app::MicroNetKwsModel model{};
 
     REQUIRE_FALSE(model.IsInited());
     REQUIRE(model.Init());
@@ -82,14 +82,14 @@
     REQUIRE(RunInferenceRandom(model));
 }
 
-TEST_CASE("Running inference with Tflu and DsCnnModel Uint8", "[DS_CNN]") {
+TEST_CASE("Running inference with Tflu and MicroNetKwsModel Int8", "[MicroNetKws]") {
     REQUIRE(NUMBER_OF_IFM_FILES == NUMBER_OF_OFM_FILES);
     for (uint32_t i = 0; i < NUMBER_OF_IFM_FILES; ++i) {
         const int8_t* input_goldenFV = get_ifm_data_array(i);
         const int8_t* output_goldenFV = get_ofm_data_array(i);
 
         DYNAMIC_SECTION("Executing inference with re-init") {
-            arm::app::DsCnnModel model{};
+            arm::app::MicroNetKwsModel model{};
 
             REQUIRE_FALSE(model.IsInited());
             REQUIRE(model.Init());
diff --git a/tests/use_case/kws_asr/InitModels.cc b/tests/use_case/kws_asr/InitModels.cc
index 770944d..97aa092 100644
--- a/tests/use_case/kws_asr/InitModels.cc
+++ b/tests/use_case/kws_asr/InitModels.cc
@@ -14,7 +14,7 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-#include "DsCnnModel.hpp"
+#include "MicroNetKwsModel.hpp"
 #include "Wav2LetterModel.hpp"
 
 #include <catch.hpp>
@@ -22,8 +22,8 @@
 /* Skip this test, Wav2LetterModel if not Vela optimized but only from ML-zoo will fail. */
 TEST_CASE("Init two Models", "[.]")
 {
-    arm::app::DsCnnModel model1;
-    arm::app::DsCnnModel model2;
+    arm::app::MicroNetKwsModel model1;
+    arm::app::MicroNetKwsModel model2;
 
     /* Ideally we should load the wav2letter model here, but there is
      * none available to run on native (ops not supported on unoptimised
diff --git a/tests/use_case/kws_asr/MfccTests.cc b/tests/use_case/kws_asr/MfccTests.cc
index 9509519..c0fb723 100644
--- a/tests/use_case/kws_asr/MfccTests.cc
+++ b/tests/use_case/kws_asr/MfccTests.cc
@@ -14,7 +14,7 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-#include "DsCnnMfcc.hpp"
+#include "MicroNetKwsMfcc.hpp"
 
 #include <algorithm>
 #include <catch.hpp>
@@ -93,17 +93,17 @@
     -22.67135, -0.61615, 2.07233, 0.58137, 1.01655, 0.85816, 0.46039, 0.03393, 1.16511, 0.0072,
 };
 
-arm::app::audio::DsCnnMFCC GetMFCCInstance() {
-    const int sampFreq = arm::app::audio::DsCnnMFCC::ms_defaultSamplingFreq;
+arm::app::audio::MicroNetMFCC GetMFCCInstance() {
+    const int sampFreq = arm::app::audio::MicroNetMFCC::ms_defaultSamplingFreq;
     const int frameLenMs = 40;
     const int frameLenSamples = sampFreq * frameLenMs * 0.001;
     const int numMfccFeats = 10;
 
-   return arm::app::audio::DsCnnMFCC(numMfccFeats, frameLenSamples);
+   return arm::app::audio::MicroNetMFCC(numMfccFeats, frameLenSamples);
 }
 
 template <class T>
-void TestQuntisedMFCC() {
+void TestQuantisedMFCC() {
     const float quantScale = 1.1088106632232666;
     const int quantOffset = 95;
     std::vector<T> mfccOutput = GetMFCCInstance().MfccComputeQuant<T>(testWav, quantScale, quantOffset);
@@ -118,9 +118,9 @@
         REQUIRE(quantizedTestWavMfcc  == Approx(mfccOutput[i]).margin(0));
     }
 }
-template void TestQuntisedMFCC<int8_t>();
-template void TestQuntisedMFCC<uint8_t>();
-template void TestQuntisedMFCC<int16_t>();
+template void TestQuantisedMFCC<int8_t>();
+template void TestQuantisedMFCC<uint8_t>();
+template void TestQuantisedMFCC<int16_t>();
 
 TEST_CASE("MFCC calculation test")
 {
@@ -141,16 +141,16 @@
 
     SECTION("int8_t")
     {
-        TestQuntisedMFCC<int8_t>();
+        TestQuantisedMFCC<int8_t>();
     }
 
     SECTION("uint8_t")
     {
-        TestQuntisedMFCC<uint8_t>();
+        TestQuantisedMFCC<uint8_t>();
     }
 
     SECTION("MFCC quant calculation test - int16_t")
     {
-        TestQuntisedMFCC<int16_t>();
+        TestQuantisedMFCC<int16_t>();
     }
 }
\ No newline at end of file