# Copyright © 2021 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 import collections MFCCParams = collections.namedtuple('MFCCParams', ['sampling_freq', 'num_fbank_bins', 'mel_lo_freq', 'mel_hi_freq', 'num_mfcc_feats', 'frame_len', 'use_htk_method', '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 spectrum_calc(self, audio_data): return np.abs(np.fft.rfft(np.hanning(self.mfcc_params.frame_len + 1)[0:self.mfcc_params.frame_len] * audio_data, self.mfcc_params.n_fft)) def log_mel(self, mel_energy): mel_energy += 1e-10 # Avoid division by zero return np.log(mel_energy) 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 frame length {self.mfcc_params.frame_len}") audio_data = np.array(audio_data) spec = self.spectrum_calc(audio_data) mel_energy = np.dot(self._np_mel_bank.astype(np.float32), np.transpose(spec).astype(np.float32)) log_mel_energy = self.log_mel(mel_energy) 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): dct_m[(k * num_fbank_bins) + n] = (np.sqrt(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 mel_norm(self, weight, right_mel, left_mel): """ Placeholder function over-ridden in child class """ return weight def create_mel_filter_bank(self): """ Creates the Mel filter bank. Returns: the mel filter bank """ # FFT calculations are greatly accelerated for frame lengths which are powers of 2 # Frames are padded and FFT bin width/length calculated accordingly 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, self.mfcc_params.use_htk_method) mel_high_freq = self.mel_scale(self.mfcc_params.mel_hi_freq, self.mfcc_params.use_htk_method) 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, self.mfcc_params.use_htk_method) 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) this_bin[i] = self.mel_norm(weight, right_mel, left_mel) 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 AudioPreprocessor: def __init__(self, mfcc, model_input_size, stride): self.model_input_size = model_input_size self.stride = stride 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: current_frame_feats = mfcc_instance.mfcc_compute(audio_data[idx:idx + int(mfcc_instance.mfcc_params.frame_len)]) features.extend(current_frame_feats) idx += self.stride def mfcc_delta_calc(self, features): """ Placeholder function over-ridden in child class """ return features def extract_features(self, audio_data): """ Extracts the MFCC features. Also calculates each features first and second order derivatives if the mfcc_delta_calc() function has been implemented by a child class. The matrix returned should be sized appropriately for input to the model, based on the model info specified in the MFCC instance. Args: 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)) features = self.mfcc_delta_calc(features) return np.float32(features)