########################################### # Project: CMSIS DSP Library # Title: MFCC.py # Description: Test pattern generation for MFCC # # $Date: 02 September 2021 # $Revision: V1.10.0 # # Target Processor: Cortex-M and Cortex-A cores # -------------------------------------------------------------------- */ # # Copyright (C) 2010-2021 ARM Limited or its affiliates. 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 # # 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. ############################################ import os.path import numpy as np import itertools import Tools import scipy import scipy.signal as sig import scipy.fftpack ################################ # # Gives the same results as the tensorflow lite # MFCC if hamming window is used # (TF stft) is using hanning by default # DEBUG = False def frequencyToMelSpace(freq): return 1127.0 * np.log(1.0 + freq / 700.0) def melSpaceToFrequency(mels): return 700.0 * (np.exp(mels / 1127.0) - 1.0) def melFilterMatrix(fmin, fmax, numOfMelFilters,fs,FFTSize): filters = np.zeros((numOfMelFilters,int(FFTSize/2+1))) zeros = np.zeros(int(FFTSize // 2 )) fmin_mel = frequencyToMelSpace(fmin) fmax_mel = frequencyToMelSpace(fmax) mels = np.linspace(fmin_mel, fmax_mel, num=numOfMelFilters+2) linearfreqs = np.linspace( 0, fs/2.0, int(FFTSize // 2 + 1) ) spectrogrammels = frequencyToMelSpace(linearfreqs)[1:] for n in range(numOfMelFilters): upper = (spectrogrammels - mels[n])/(mels[n+1]-mels[n]) lower = (mels[n+2] - spectrogrammels)/(mels[n+2]-mels[n+1]) filters[n, :] = np.hstack([0,np.maximum(zeros,np.minimum(upper,lower))]) return filters def dctMatrix(numOfDctOutputs, numOfMelFilters): result = np.zeros((numOfDctOutputs,numOfMelFilters)) s=(np.linspace(1,numOfMelFilters,numOfMelFilters) - 0.5)/numOfMelFilters for i in range(0, numOfDctOutputs): result[i,:]=np.cos(i * np.pi*s) * np.sqrt(2.0/numOfMelFilters) return result class MFCCConfig: def __init__(self,freq_min,freq_high,numOfMelFilters,numOfDctOutputs,FFTSize,sample_rate): self._freq_min=freq_min self._freq_high=freq_high self._numOfMelFilters = numOfMelFilters self._FFTSize=FFTSize self._sample_rate=sample_rate #self._window = sig.hann(FFTSize, sym=True) self._window = sig.hamming(FFTSize, sym=False) #print(self._window) self._numOfDctOutputs=numOfDctOutputs self._filters = melFilterMatrix(freq_min, freq_high, numOfMelFilters,sample_rate,FFTSize) self._dctMatrixFilters = dctMatrix(numOfDctOutputs, numOfMelFilters) def mfcc(self,audio): m = np.amax(np.abs(audio)) if m != 0: s = 1.0 / m else: s = 1.0 audio = audio * s audioWin = audio * self._window if DEBUG: print(audioWin) audioFFT = scipy.fftpack.fft(audioWin) if DEBUG: print(audioFFT) audioPower = np.abs(audioFFT) if DEBUG: print(audioPower) filterLimit = int(1 + self._FFTSize // 2) audioPower=audioPower[:filterLimit] audioFiltered = np.dot(self._filters,audioPower) if DEBUG: print(audioFiltered) audioLog = np.log(audioFiltered + 1e-6) cepstral_coefficents = np.dot(self._dctMatrixFilters, audioLog) return(cepstral_coefficents) debug=np.array([ 0.65507051 ,-0.94647589 ,0.00627239 ,0.14151286 ,-0.10863318 ,-0.36370327 ,0.05777126 ,-0.11915792 ,0.50183546 ,-0.31461335 ,0.66440771 ,0.05389963 ,0.39690544 ,0.25424852 ,-0.17045277 ,0.09649268 ,0.87357385 ,-0.44666372 ,-0.02637822 ,-0.10055151 ,-0.14610252 ,-0.05981251 ,-0.02999124 ,0.60923213 ,0.10530095 ,0.35684248 ,0.21845946 ,0.47845017 ,-0.60206979 ,0.25186908 ,-0.27410056 ,-0.07080467 ,-0.05109539 ,-0.2666572 ,0.25483105 ,-0.86459185 ,0.07733397 ,-0.58535444 ,0.06230904 ,-0.04161475 ,-0.17467296 ,0.77721125 ,-0.01728161 ,-0.32141218 ,0.36674466 ,-0.17932843 ,0.78486115 ,0.12469579 ,-0.94796877 ,0.05536031 ,0.32627676 ,0.46628512 ,-0.02585836 ,-0.51439834 ,0.21387904 ,0.16319442 ,-0.01020818 ,-0.77161183 ,0.07754634 ,-0.24970455 ,0.2368003 ,0.35167963 ,0.14620137 ,-0.02415204 ,0.91086167 ,-0.02434647 ,-0.3968239 ,-0.04703925 ,-0.43905103 ,-0.34834965 ,0.33728158 ,0.15138992 ,-0.43218885 ,0.26619718 ,0.07177906 ,0.33393581 ,-0.50306915 ,-0.63101084 ,-0.08128395 ,-0.06569788 ,0.84232797 ,-0.32436751 ,0.02528537 ,-0.3498329 ,0.41859931 ,0.07794887 ,0.4571989 ,0.24290963 ,0.08437417 ,-0.01371585 ,-0.00103008 ,0.83978697 ,-0.29001237 ,0.14438743 ,0.11943318 ,-0.25576402 ,0.25151083 ,0.07886626 ,0.11565781 ,-0.01582203 ,0.1310246 ,-0.5553611 ,-0.37950665 ,0.44179691 ,0.08460877 ,0.30646419 ,0.48927934 ,-0.21240309 ,0.36844264 ,0.49686615 ,-0.81617664 ,0.52221472 ,-0.05188992 ,-0.03929655 ,-0.47674501 ,-0.54506781 ,0.30711148 ,0.10049337 ,-0.47549213 ,0.59106713 ,-0.62276051 ,-0.35182917 ,0.14612027 ,0.56142168 ,-0.01053732 ,0.35782179 ,-0.27220781 ,-0.03672346 ,-0.11282222 ,0.3364912 ,-0.22352515 ,-0.04245287 ,0.56968605 ,-0.14023724 ,-0.82982905 ,0.00860008 ,0.37920345 ,-0.53749318 ,-0.12761215 ,0.08567603 ,0.47020765 ,-0.28794812 ,-0.33888971 ,0.01850441 ,0.66848233 ,-0.26532759 ,-0.20777571 ,-0.68342729 ,-0.41498696 ,0.00593224 ,0.02229368 ,0.75596329 ,0.29447568 ,-0.1106449 ,0.24181939 ,0.05807497 ,-0.14343857 ,0.304988 ,0.00689148 ,-0.06264758 ,0.25864714 ,-0.22252155 ,0.28621689 ,0.17031599 ,-0.34694027 ,-0.01625718 ,0.39834181 ,0.01259659 ,-0.28022716 ,-0.02506168 ,-0.10276881 ,0.31733924 ,0.02787068 ,-0.09824124 ,0.45147797 ,0.14451518 ,0.17996395 ,-0.70594978 ,-0.92943177 ,0.13649282 ,-0.5938426 ,0.50289928 ,0.19635269 ,0.16811504 ,0.05803999 ,0.0037204 ,0.13847419 ,0.30568038 ,0.3700732 ,0.21257548 ,-0.31151753 ,-0.28836886 ,0.68743932 ,-0.11084429 ,-0.4673766 ,0.16637754 ,-0.38992572 ,0.16505578 ,-0.07499844 ,0.04226538 ,-0.11042177 ,0.0704542 ,-0.632819 ,-0.54898472 ,0.26498649 ,-0.59380386 ,0.93387213 ,0.06526726 ,-0.23223558 ,0.07941394 ,0.14325166 ,0.26914661 ,0.00925575 ,-0.34282161 ,-0.51418231 ,-0.12011075 ,-0.26676314 ,-0.09999028 ,0.03027513 ,0.22846503 ,-0.08930338 ,-0.1867156 ,0.66297846 ,0.32220769 ,-0.06015469 ,0.04034043 ,0.09595597 ,-1. ,-0.42933352 ,0.25069376 ,-0.26030918 ,-0.28511861 ,-0.19931228 ,0.24408572 ,-0.3231952 ,0.45688981 ,-0.07354078 ,0.25669449 ,-0.44202722 ,0.11928406 ,-0.32826109 ,0.52660984 ,0.03067858 ,0.11095242 ,0.19933679 ,0.03042371 ,-0.34768682 ,0.09108447 ,0.61234556 ,0.1854931 ,0.19680502 ,0.27617564 ,0.33381827 ,-0.47358967 ,0.28714328 ,-0.27495982]) def noiseSignal(nb): return(2.0*np.random.rand(nb)-1.0) def sineSignal(freqRatio,nb): fc = nb / 2.0 f = freqRatio*fc time = np.arange(0,nb) return(np.sin(2 * np.pi * f * time/nb)) def noisySineSignal(noiseAmp,r,nb): return(noiseAmp*noiseSignal(nb) + r*sineSignal(r,nb)) def writeTests(config,format): NBSAMPLES=[256,512,1024] if DEBUG: NBSAMPLES=[256] sample_rate = 16000 FFTSize = 256 numOfDctOutputs = 13 freq_min = 64 freq_high = sample_rate / 2 numOfMelFilters = 20 for nb in NBSAMPLES: inputsNoise=[] inputsSine=[] outputsNoise=[] outputsSine=[] inNoiselengths=[] outNoiselengths=[] inSinelengths=[] outSinelengths=[] FFTSize=nb mfccConfig=MFCCConfig(freq_min,freq_high,numOfMelFilters,numOfDctOutputs,FFTSize,sample_rate) # Add noise audio=np.random.randn(nb) audio = Tools.normalize(audio) if DEBUG: audio=debug inputsNoise += list(audio) refNoise=mfccConfig.mfcc(audio) if format == Tools.Q15: refNoise = refNoise / (1<<8) if format == Tools.Q31: refNoise = refNoise / (1<<8) #print(audio) if DEBUG: print(refNoise) outputsNoise+=list(refNoise) inNoiselengths+=[nb] outNoiselengths+=[numOfDctOutputs] config.writeInput(1, inputsNoise,"MFCCNoiseInput_%d_" % nb) config.writeReference(1, outputsNoise,"MFCCNoiseRef_%d_" % nb) # Sine audio=noisySineSignal(0.1,0.8,nb) #audio = Tools.normalize(audio) inputsSine += list(audio) refSine=mfccConfig.mfcc(audio) if format == Tools.Q15: refSine = refSine / (1<<8) if format == Tools.Q31: refSine = refSine / (1<<8) #print(audio) outputsSine+=list(refSine) inSinelengths+=[nb] outSinelengths+=[numOfDctOutputs] config.writeInput(1, inputsSine,"MFCCSineInput_%d_" % nb) config.writeReference(1, outputsSine,"MFCCSineRef_%d_" % nb) def generatePatterns(): PATTERNDIR = os.path.join("Patterns","DSP","Transform","MFCC") PARAMDIR = os.path.join("Parameters","DSP","Transform","MFCC") configf32=Tools.Config(PATTERNDIR,PARAMDIR,"f32") configf16=Tools.Config(PATTERNDIR,PARAMDIR,"f16") configq31=Tools.Config(PATTERNDIR,PARAMDIR,"q31") configq15=Tools.Config(PATTERNDIR,PARAMDIR,"q15") #configq7=Tools.Config(PATTERNDIR,PARAMDIR,"q7") configf32.setOverwrite(False) configf16.setOverwrite(False) configq31.setOverwrite(False) configq15.setOverwrite(False) writeTests(configf32,0) writeTests(configf16,Tools.F16) writeTests(configq31,Tools.Q31) writeTests(configq15,Tools.Q15) if __name__ == '__main__': generatePatterns()