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