| # Copyright © 2020 Arm Ltd and Contributors. All rights reserved. |
| # SPDX-License-Identifier: MIT |
| |
| """Class used to extract the Mel-frequency cepstral coefficients from a given audio frame.""" |
| |
| import numpy as np |
| |
| |
| class MFCCParams: |
| def __init__(self, sampling_freq, num_fbank_bins, |
| mel_lo_freq, mel_hi_freq, num_mfcc_feats, frame_len, use_htk_method, n_FFT): |
| self.sampling_freq = sampling_freq |
| self.num_fbank_bins = num_fbank_bins |
| self.mel_lo_freq = mel_lo_freq |
| self.mel_hi_freq = mel_hi_freq |
| self.num_mfcc_feats = num_mfcc_feats |
| self.frame_len = frame_len |
| self.use_htk_method = use_htk_method |
| self.n_FFT = n_FFT |
| |
| |
| class MFCC: |
| |
| def __init__(self, mfcc_params): |
| self.mfcc_params = mfcc_params |
| self.FREQ_STEP = 200.0 / 3 |
| self.MIN_LOG_HZ = 1000.0 |
| self.MIN_LOG_MEL = self.MIN_LOG_HZ / self.FREQ_STEP |
| self.LOG_STEP = 1.8562979903656 / 27.0 |
| self.__frame_len_padded = int(2 ** (np.ceil((np.log(self.mfcc_params.frame_len) / np.log(2.0))))) |
| self.__filter_bank_initialised = False |
| self.__frame = np.zeros(self.__frame_len_padded) |
| self.__buffer = np.zeros(self.__frame_len_padded) |
| self.__filter_bank_filter_first = np.zeros(self.mfcc_params.num_fbank_bins) |
| self.__filter_bank_filter_last = np.zeros(self.mfcc_params.num_fbank_bins) |
| self.__mel_energies = np.zeros(self.mfcc_params.num_fbank_bins) |
| self.__dct_matrix = self.create_dct_matrix(self.mfcc_params.num_fbank_bins, self.mfcc_params.num_mfcc_feats) |
| self.__mel_filter_bank = self.create_mel_filter_bank() |
| self.__np_mel_bank = np.zeros([self.mfcc_params.num_fbank_bins, int(self.mfcc_params.n_FFT / 2) + 1]) |
| |
| for i in range(self.mfcc_params.num_fbank_bins): |
| k = 0 |
| for j in range(int(self.__filter_bank_filter_first[i]), int(self.__filter_bank_filter_last[i]) + 1): |
| self.__np_mel_bank[i, j] = self.__mel_filter_bank[i][k] |
| k += 1 |
| |
| def mel_scale(self, freq, use_htk_method): |
| """ |
| Gets the mel scale for a particular sample frequency. |
| |
| Args: |
| freq: The sampling frequency. |
| use_htk_method: Boolean to set whether to use HTK method or not. |
| |
| Returns: |
| the mel scale |
| """ |
| if use_htk_method: |
| return 1127.0 * np.log(1.0 + freq / 700.0) |
| else: |
| mel = freq / self.FREQ_STEP |
| |
| if freq >= self.MIN_LOG_HZ: |
| mel = self.MIN_LOG_MEL + np.log(freq / self.MIN_LOG_HZ) / self.LOG_STEP |
| return mel |
| |
| def inv_mel_scale(self, mel_freq, use_htk_method): |
| """ |
| Gets the sample frequency for a particular mel. |
| |
| Args: |
| mel_freq: The mel frequency. |
| use_htk_method: Boolean to set whether to use HTK method or not. |
| |
| Returns: |
| the sample frequency |
| """ |
| if use_htk_method: |
| return 700.0 * (np.exp(mel_freq / 1127.0) - 1.0) |
| else: |
| freq = self.FREQ_STEP * mel_freq |
| |
| if mel_freq >= self.MIN_LOG_MEL: |
| freq = self.MIN_LOG_HZ * np.exp(self.LOG_STEP * (mel_freq - self.MIN_LOG_MEL)) |
| return freq |
| |
| def mfcc_compute(self, audio_data): |
| """ |
| Extracts the MFCC for a single frame. |
| |
| Args: |
| audio_data: The audio data to process. |
| |
| Returns: |
| the MFCC features |
| """ |
| if len(audio_data) != self.mfcc_params.frame_len: |
| raise ValueError( |
| f"audio_data buffer size {len(audio_data)} does not match the frame length {self.mfcc_params.frame_len}") |
| |
| audio_data = np.array(audio_data) |
| spec = np.abs(np.fft.rfft(np.hanning(self.mfcc_params.n_FFT + 1)[0:self.mfcc_params.n_FFT] * audio_data, |
| self.mfcc_params.n_FFT)) ** 2 |
| mel_energy = np.dot(self.__np_mel_bank.astype(np.float32), |
| np.transpose(spec).astype(np.float32)) |
| |
| mel_energy += 1e-10 |
| log_mel_energy = 10.0 * np.log10(mel_energy) |
| top_db = 80.0 |
| |
| log_mel_energy = np.maximum(log_mel_energy, log_mel_energy.max() - top_db) |
| |
| mfcc_feats = np.dot(self.__dct_matrix, log_mel_energy) |
| |
| return mfcc_feats |
| |
| def create_dct_matrix(self, num_fbank_bins, num_mfcc_feats): |
| """ |
| Creates the Discrete Cosine Transform matrix to be used in the compute function. |
| |
| Args: |
| num_fbank_bins: The number of filter bank bins |
| num_mfcc_feats: the number of MFCC features |
| |
| Returns: |
| the DCT matrix |
| """ |
| dct_m = np.zeros(num_fbank_bins * num_mfcc_feats) |
| for k in range(num_mfcc_feats): |
| for n in range(num_fbank_bins): |
| if k == 0: |
| dct_m[(k * num_fbank_bins) + n] = 2 * np.sqrt(1 / (4 * num_fbank_bins)) * np.cos( |
| (np.pi / num_fbank_bins) * (n + 0.5) * k) |
| else: |
| dct_m[(k * num_fbank_bins) + n] = 2 * np.sqrt(1 / (2 * num_fbank_bins)) * np.cos( |
| (np.pi / num_fbank_bins) * (n + 0.5) * k) |
| |
| dct_m = np.reshape(dct_m, [self.mfcc_params.num_mfcc_feats, self.mfcc_params.num_fbank_bins]) |
| return dct_m |
| |
| def create_mel_filter_bank(self): |
| """ |
| Creates the Mel filter bank. |
| |
| Returns: |
| the mel filter bank |
| """ |
| num_fft_bins = int(self.__frame_len_padded / 2) |
| fft_bin_width = self.mfcc_params.sampling_freq / self.__frame_len_padded |
| |
| mel_low_freq = self.mel_scale(self.mfcc_params.mel_lo_freq, False) |
| mel_high_freq = self.mel_scale(self.mfcc_params.mel_hi_freq, False) |
| mel_freq_delta = (mel_high_freq - mel_low_freq) / (self.mfcc_params.num_fbank_bins + 1) |
| |
| this_bin = np.zeros(num_fft_bins) |
| mel_fbank = [0] * self.mfcc_params.num_fbank_bins |
| |
| for bin_num in range(self.mfcc_params.num_fbank_bins): |
| left_mel = mel_low_freq + bin_num * mel_freq_delta |
| center_mel = mel_low_freq + (bin_num + 1) * mel_freq_delta |
| right_mel = mel_low_freq + (bin_num + 2) * mel_freq_delta |
| first_index = last_index = -1 |
| |
| for i in range(num_fft_bins): |
| freq = (fft_bin_width * i) |
| mel = self.mel_scale(freq, False) |
| this_bin[i] = 0.0 |
| |
| if (mel > left_mel) and (mel < right_mel): |
| if mel <= center_mel: |
| weight = (mel - left_mel) / (center_mel - left_mel) |
| else: |
| weight = (right_mel - mel) / (right_mel - center_mel) |
| |
| enorm = 2.0 / (self.inv_mel_scale(right_mel, False) - self.inv_mel_scale(left_mel, False)) |
| weight *= enorm |
| this_bin[i] = weight |
| |
| if first_index == -1: |
| first_index = i |
| last_index = i |
| |
| self.__filter_bank_filter_first[bin_num] = first_index |
| self.__filter_bank_filter_last[bin_num] = last_index |
| mel_fbank[bin_num] = np.zeros(last_index - first_index + 1) |
| j = 0 |
| |
| for i in range(first_index, last_index + 1): |
| mel_fbank[bin_num][j] = this_bin[i] |
| j += 1 |
| |
| return mel_fbank |
| |
| |
| class Preprocessor: |
| |
| def __init__(self, mfcc, model_input_size, stride): |
| self.model_input_size = model_input_size |
| self.stride = stride |
| |
| # Savitzky - Golay differential filters |
| self.__savgol_order1_coeffs = np.array([6.66666667e-02, 5.00000000e-02, 3.33333333e-02, |
| 1.66666667e-02, -3.46944695e-18, -1.66666667e-02, |
| -3.33333333e-02, -5.00000000e-02, -6.66666667e-02]) |
| |
| self.savgol_order2_coeffs = np.array([0.06060606, 0.01515152, -0.01731602, |
| -0.03679654, -0.04329004, -0.03679654, |
| -0.01731602, 0.01515152, 0.06060606]) |
| |
| self.__mfcc_calc = mfcc |
| |
| def __normalize(self, values): |
| """ |
| Normalize values to mean 0 and std 1 |
| """ |
| ret_val = (values - np.mean(values)) / np.std(values) |
| return ret_val |
| |
| def __get_features(self, features, mfcc_instance, audio_data): |
| idx = 0 |
| while len(features) < self.model_input_size * mfcc_instance.mfcc_params.num_mfcc_feats: |
| features.extend(mfcc_instance.mfcc_compute(audio_data[idx:idx + int(mfcc_instance.mfcc_params.frame_len)])) |
| idx += self.stride |
| |
| def extract_features(self, audio_data): |
| """ |
| Extracts the MFCC features, and calculates each features first and second order derivative. |
| The matrix returned should be sized appropriately for input to the model, based |
| on the model info specified in the MFCC instance. |
| |
| Args: |
| mfcc_instance: The instance of MFCC used for this calculation |
| audio_data: the audio data to be used for this calculation |
| Returns: |
| the derived MFCC feature vector, sized appropriately for inference |
| """ |
| |
| num_samples_per_inference = ((self.model_input_size - 1) |
| * self.stride) + self.__mfcc_calc.mfcc_params.frame_len |
| if len(audio_data) < num_samples_per_inference: |
| raise ValueError("audio_data size for feature extraction is smaller than " |
| "the expected number of samples needed for inference") |
| |
| features = [] |
| self.__get_features(features, self.__mfcc_calc, np.asarray(audio_data)) |
| features = np.reshape(np.array(features), (self.model_input_size, self.__mfcc_calc.mfcc_params.num_mfcc_feats)) |
| |
| mfcc_delta_np = np.zeros_like(features) |
| mfcc_delta2_np = np.zeros_like(features) |
| |
| for i in range(features.shape[1]): |
| idelta = np.convolve(features[:, i], self.__savgol_order1_coeffs, 'same') |
| mfcc_delta_np[:, i] = (idelta) |
| ideltadelta = np.convolve(features[:, i], self.savgol_order2_coeffs, 'same') |
| mfcc_delta2_np[:, i] = (ideltadelta) |
| |
| features = np.concatenate((self.__normalize(features), self.__normalize(mfcc_delta_np), |
| self.__normalize(mfcc_delta2_np)), axis=1) |
| |
| return np.float32(features) |