alexander | f42f568 | 2021-07-16 11:30:56 +0100 | [diff] [blame] | 1 | # Copyright © 2021 Arm Ltd and Contributors. All rights reserved. |
| 2 | # SPDX-License-Identifier: MIT |
| 3 | |
| 4 | """Class used to extract the Mel-frequency cepstral coefficients from a given audio frame.""" |
| 5 | |
| 6 | import numpy as np |
| 7 | import collections |
| 8 | |
| 9 | MFCCParams = collections.namedtuple('MFCCParams', ['sampling_freq', 'num_fbank_bins', 'mel_lo_freq', 'mel_hi_freq', |
| 10 | 'num_mfcc_feats', 'frame_len', 'use_htk_method', 'n_fft']) |
| 11 | |
| 12 | |
| 13 | class MFCC: |
| 14 | |
| 15 | def __init__(self, mfcc_params): |
| 16 | self.mfcc_params = mfcc_params |
| 17 | self.FREQ_STEP = 200.0 / 3 |
| 18 | self.MIN_LOG_HZ = 1000.0 |
| 19 | self.MIN_LOG_MEL = self.MIN_LOG_HZ / self.FREQ_STEP |
| 20 | self.LOG_STEP = 1.8562979903656 / 27.0 |
| 21 | self._frame_len_padded = int(2 ** (np.ceil((np.log(self.mfcc_params.frame_len) / np.log(2.0))))) |
| 22 | self._filter_bank_initialised = False |
| 23 | self.__frame = np.zeros(self._frame_len_padded) |
| 24 | self.__buffer = np.zeros(self._frame_len_padded) |
| 25 | self._filter_bank_filter_first = np.zeros(self.mfcc_params.num_fbank_bins) |
| 26 | self._filter_bank_filter_last = np.zeros(self.mfcc_params.num_fbank_bins) |
| 27 | self.__mel_energies = np.zeros(self.mfcc_params.num_fbank_bins) |
| 28 | self._dct_matrix = self.create_dct_matrix(self.mfcc_params.num_fbank_bins, self.mfcc_params.num_mfcc_feats) |
| 29 | self.__mel_filter_bank = self.create_mel_filter_bank() |
| 30 | self._np_mel_bank = np.zeros([self.mfcc_params.num_fbank_bins, int(self.mfcc_params.n_fft / 2) + 1]) |
| 31 | |
| 32 | for i in range(self.mfcc_params.num_fbank_bins): |
| 33 | k = 0 |
| 34 | for j in range(int(self._filter_bank_filter_first[i]), int(self._filter_bank_filter_last[i]) + 1): |
| 35 | self._np_mel_bank[i, j] = self.__mel_filter_bank[i][k] |
| 36 | k += 1 |
| 37 | |
| 38 | def mel_scale(self, freq, use_htk_method): |
| 39 | """ |
| 40 | Gets the mel scale for a particular sample frequency. |
| 41 | |
| 42 | Args: |
| 43 | freq: The sampling frequency. |
| 44 | use_htk_method: Boolean to set whether to use HTK method or not. |
| 45 | |
| 46 | Returns: |
| 47 | the mel scale |
| 48 | """ |
| 49 | if use_htk_method: |
| 50 | return 1127.0 * np.log(1.0 + freq / 700.0) |
| 51 | else: |
| 52 | mel = freq / self.FREQ_STEP |
| 53 | |
| 54 | if freq >= self.MIN_LOG_HZ: |
| 55 | mel = self.MIN_LOG_MEL + np.log(freq / self.MIN_LOG_HZ) / self.LOG_STEP |
| 56 | return mel |
| 57 | |
| 58 | def inv_mel_scale(self, mel_freq, use_htk_method): |
| 59 | """ |
| 60 | Gets the sample frequency for a particular mel. |
| 61 | |
| 62 | Args: |
| 63 | mel_freq: The mel frequency. |
| 64 | use_htk_method: Boolean to set whether to use HTK method or not. |
| 65 | |
| 66 | Returns: |
| 67 | the sample frequency |
| 68 | """ |
| 69 | if use_htk_method: |
| 70 | return 700.0 * (np.exp(mel_freq / 1127.0) - 1.0) |
| 71 | else: |
| 72 | freq = self.FREQ_STEP * mel_freq |
| 73 | |
| 74 | if mel_freq >= self.MIN_LOG_MEL: |
| 75 | freq = self.MIN_LOG_HZ * np.exp(self.LOG_STEP * (mel_freq - self.MIN_LOG_MEL)) |
| 76 | return freq |
| 77 | |
| 78 | def spectrum_calc(self, audio_data): |
| 79 | return np.abs(np.fft.rfft(np.hanning(self.mfcc_params.frame_len + 1)[0:self.mfcc_params.frame_len] * audio_data, |
| 80 | self.mfcc_params.n_fft)) |
| 81 | |
| 82 | def log_mel(self, mel_energy): |
| 83 | mel_energy += 1e-10 # Avoid division by zero |
| 84 | return np.log(mel_energy) |
| 85 | |
| 86 | def mfcc_compute(self, audio_data): |
| 87 | """ |
| 88 | Extracts the MFCC for a single frame. |
| 89 | |
| 90 | Args: |
| 91 | audio_data: The audio data to process. |
| 92 | |
| 93 | Returns: |
| 94 | the MFCC features |
| 95 | """ |
| 96 | if len(audio_data) != self.mfcc_params.frame_len: |
| 97 | raise ValueError( |
| 98 | f"audio_data buffer size {len(audio_data)} does not match frame length {self.mfcc_params.frame_len}") |
| 99 | |
| 100 | audio_data = np.array(audio_data) |
| 101 | spec = self.spectrum_calc(audio_data) |
| 102 | mel_energy = np.dot(self._np_mel_bank.astype(np.float32), |
| 103 | np.transpose(spec).astype(np.float32)) |
| 104 | log_mel_energy = self.log_mel(mel_energy) |
| 105 | mfcc_feats = np.dot(self._dct_matrix, log_mel_energy) |
| 106 | return mfcc_feats |
| 107 | |
| 108 | def create_dct_matrix(self, num_fbank_bins, num_mfcc_feats): |
| 109 | """ |
| 110 | Creates the Discrete Cosine Transform matrix to be used in the compute function. |
| 111 | |
| 112 | Args: |
| 113 | num_fbank_bins: The number of filter bank bins |
| 114 | num_mfcc_feats: the number of MFCC features |
| 115 | |
| 116 | Returns: |
| 117 | the DCT matrix |
| 118 | """ |
| 119 | |
| 120 | dct_m = np.zeros(num_fbank_bins * num_mfcc_feats) |
| 121 | for k in range(num_mfcc_feats): |
| 122 | for n in range(num_fbank_bins): |
| 123 | dct_m[(k * num_fbank_bins) + n] = (np.sqrt(2 / num_fbank_bins)) * np.cos( |
| 124 | (np.pi / num_fbank_bins) * (n + 0.5) * k) |
| 125 | dct_m = np.reshape(dct_m, [self.mfcc_params.num_mfcc_feats, self.mfcc_params.num_fbank_bins]) |
| 126 | return dct_m |
| 127 | |
| 128 | def mel_norm(self, weight, right_mel, left_mel): |
| 129 | """ |
| 130 | Placeholder function over-ridden in child class |
| 131 | """ |
| 132 | return weight |
| 133 | |
| 134 | def create_mel_filter_bank(self): |
| 135 | """ |
| 136 | Creates the Mel filter bank. |
| 137 | |
| 138 | Returns: |
| 139 | the mel filter bank |
| 140 | """ |
| 141 | # FFT calculations are greatly accelerated for frame lengths which are powers of 2 |
| 142 | # Frames are padded and FFT bin width/length calculated accordingly |
| 143 | num_fft_bins = int(self._frame_len_padded / 2) |
| 144 | fft_bin_width = self.mfcc_params.sampling_freq / self._frame_len_padded |
| 145 | |
| 146 | mel_low_freq = self.mel_scale(self.mfcc_params.mel_lo_freq, self.mfcc_params.use_htk_method) |
| 147 | mel_high_freq = self.mel_scale(self.mfcc_params.mel_hi_freq, self.mfcc_params.use_htk_method) |
| 148 | mel_freq_delta = (mel_high_freq - mel_low_freq) / (self.mfcc_params.num_fbank_bins + 1) |
| 149 | |
| 150 | this_bin = np.zeros(num_fft_bins) |
| 151 | mel_fbank = [0] * self.mfcc_params.num_fbank_bins |
| 152 | for bin_num in range(self.mfcc_params.num_fbank_bins): |
| 153 | left_mel = mel_low_freq + bin_num * mel_freq_delta |
| 154 | center_mel = mel_low_freq + (bin_num + 1) * mel_freq_delta |
| 155 | right_mel = mel_low_freq + (bin_num + 2) * mel_freq_delta |
| 156 | first_index = last_index = -1 |
| 157 | |
| 158 | for i in range(num_fft_bins): |
| 159 | freq = (fft_bin_width * i) |
| 160 | mel = self.mel_scale(freq, self.mfcc_params.use_htk_method) |
| 161 | this_bin[i] = 0.0 |
| 162 | |
| 163 | if (mel > left_mel) and (mel < right_mel): |
| 164 | if mel <= center_mel: |
| 165 | weight = (mel - left_mel) / (center_mel - left_mel) |
| 166 | else: |
| 167 | weight = (right_mel - mel) / (right_mel - center_mel) |
| 168 | |
| 169 | this_bin[i] = self.mel_norm(weight, right_mel, left_mel) |
| 170 | |
| 171 | if first_index == -1: |
| 172 | first_index = i |
| 173 | last_index = i |
| 174 | |
| 175 | self._filter_bank_filter_first[bin_num] = first_index |
| 176 | self._filter_bank_filter_last[bin_num] = last_index |
| 177 | mel_fbank[bin_num] = np.zeros(last_index - first_index + 1) |
| 178 | j = 0 |
| 179 | |
| 180 | for i in range(first_index, last_index + 1): |
| 181 | mel_fbank[bin_num][j] = this_bin[i] |
| 182 | j += 1 |
| 183 | |
| 184 | return mel_fbank |
| 185 | |
| 186 | |
| 187 | class AudioPreprocessor: |
| 188 | |
| 189 | def __init__(self, mfcc, model_input_size, stride): |
| 190 | self.model_input_size = model_input_size |
| 191 | self.stride = stride |
| 192 | self._mfcc_calc = mfcc |
| 193 | |
| 194 | def _normalize(self, values): |
| 195 | """ |
| 196 | Normalize values to mean 0 and std 1 |
| 197 | """ |
| 198 | ret_val = (values - np.mean(values)) / np.std(values) |
| 199 | return ret_val |
| 200 | |
| 201 | def _get_features(self, features, mfcc_instance, audio_data): |
| 202 | idx = 0 |
| 203 | while len(features) < self.model_input_size * mfcc_instance.mfcc_params.num_mfcc_feats: |
| 204 | current_frame_feats = mfcc_instance.mfcc_compute(audio_data[idx:idx + int(mfcc_instance.mfcc_params.frame_len)]) |
| 205 | features.extend(current_frame_feats) |
| 206 | idx += self.stride |
| 207 | |
| 208 | def mfcc_delta_calc(self, features): |
| 209 | """ |
| 210 | Placeholder function over-ridden in child class |
| 211 | """ |
| 212 | return features |
| 213 | |
| 214 | def extract_features(self, audio_data): |
| 215 | """ |
| 216 | Extracts the MFCC features. Also calculates each features first and second order derivatives |
| 217 | if the mfcc_delta_calc() function has been implemented by a child class. |
| 218 | The matrix returned should be sized appropriately for input to the model, based |
| 219 | on the model info specified in the MFCC instance. |
| 220 | |
| 221 | Args: |
| 222 | audio_data: the audio data to be used for this calculation |
| 223 | Returns: |
| 224 | the derived MFCC feature vector, sized appropriately for inference |
| 225 | """ |
| 226 | |
| 227 | num_samples_per_inference = ((self.model_input_size - 1) |
| 228 | * self.stride) + self._mfcc_calc.mfcc_params.frame_len |
| 229 | |
| 230 | if len(audio_data) < num_samples_per_inference: |
| 231 | raise ValueError("audio_data size for feature extraction is smaller than " |
| 232 | "the expected number of samples needed for inference") |
| 233 | |
| 234 | features = [] |
| 235 | self._get_features(features, self._mfcc_calc, np.asarray(audio_data)) |
| 236 | features = np.reshape(np.array(features), (self.model_input_size, self._mfcc_calc.mfcc_params.num_mfcc_feats)) |
| 237 | features = self.mfcc_delta_calc(features) |
| 238 | return np.float32(features) |