261 lines
10 KiB
Python
261 lines
10 KiB
Python
# 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)
|