Python features.mfcc函数代码示例

def load_validation_set():
    """
    Output
        a tuple of features: (fft features, mfcc features, mean-std features)
    Description
        extracts three types of features from validation set.
    """
    ffts = dict()
    mfccs = dict()
    mean_stds = dict()

    for i in validation_ids:
        path = './validation/validation.{i}.wav'.format(i=i)

        _, X = read_wav(path)

        # FFT
        fft = np.array(abs(sp.fft(X)[:1000]))
        ffts.update({i: fft})

        # MFCC
        ceps, mspec, spec = mfcc(X)
        num_ceps = len(ceps)
        x = np.mean(ceps[int(num_ceps*1/10):int(num_ceps*9/10)], axis=0)
        mfccs.update({i: x})


        # Mean-Std
        [Fs, x] = audioBasicIO.readAudioFile(path);
        F = audioFeatureExtraction.stFeatureExtraction(x, Fs, 0.050*Fs, 0.025*Fs);
        mean_std = []
        for f in F:
            mean_std.extend([f.mean(), f.std()])
        mean_stds.update({i: np.array(mean_std)})
    return (ffts, mfccs, mean_stds)

def BED_extract(path, nfft=784):
	list_data = numpy.array([])
	list_label = numpy.array([])
	#W:anger:0 N:neutral:1 F:happiness:2 T:sadness:3
	dic = {'W':0,'L':1,'E':3,'A':0,'F':2,'T':3,'N':1}

	for root, dir, files in os.walk(path):

		rootpath = os.path.join(os.path.abspath(path), root)

		for file in files:
			if os.path.splitext(file)[1].lower()=='.wav':
				filepath = os.path.join(rootpath, file)

				SR, X = wavfile.read(filepath)

				_, _, spec = mfcc(X, fs=SR, nfft=(nfft*2))

				print(filepath)

				list_data = numpy.append(list_data, numpy.mean(spec, axis=0)[:nfft]/numpy.max(spec))
				list_label = numpy.append(list_label, int(dic[file[5]]))

	list_data = numpy.reshape(list_data, (len(list_data)/nfft, nfft))

	return list_data, list_label

    def apply(self, data):
        all_ceps = []
        for ch in data:
            ceps, mspec, spec = mfcc(ch)
            all_ceps.append(ceps.ravel())

        return np.array(all_ceps)

def getmfccdata(path):
    """
    This function extracts the mfcc data from the wav files

    Parameters:
    -----------
    path - path to get the directory name of the songs present "E:\UNM\CS 529 - Intro to Machine Learning\Assignment 3\opihi.cs.uvic.ca\sound\genres"

    Returns:
    --------
    mfccdata - mfcc data matrix of size (600,13)
    """
    classesmatrix = np.zeros((no_of_docs, 1))                       # Stores the song, genre information in classesmatrix.txt file -> Line number as song index, genre
    mfccdata = np.zeros((no_of_docs, no_of_mfcc_features))          # Matrix (600,13) to store the fft features information of all the songs in 6 genres
    fileindex = 0                                                   # to store the current offset of the song
    for subdir, dirs, files in os.walk(path):                       # Traversing all the files in 6 genres
        if os.path.basename(subdir) in genres.keys():
            for f in files:
                if f.endswith('.wav'):
                    print "Processing file : " + f
                    sample_rate, X = scipy.io.wavfile.read(os.path.join(subdir, f))
                    ceps, mspec, spec = mfcc(X)
                    num_ceps = ceps.shape[0]
                    mfcc_features = np.mean(ceps[int(num_ceps * 1 / 10):int(num_ceps * 9 / 10)], axis=0)   # Extracts 13 features.
                    for i in range(len(mfcc_features)):
                        mfccdata[fileindex][i] = mfcc_features[i]
                    classesmatrix[fileindex] = genres[os.path.basename(subdir)]     # Storing the genre of every song in a matrix.
                    fileindex += 1
    np.savetxt('classesmatrix.txt', classesmatrix, '%d')                        # Writing the classesmatrix to a file.
    return mfccdata

def BED_extract(path, nfft):
  list_data = numpy.array([])
  list_label = numpy.array([])
  
  """
  dic = {'W':[1,0],'L':[0,1],'E':[0,1],'A':[0,1],'F':[1,0],'T':[0,1],'N':[0.5,0.5]}
  """
  dic = {'W':[0,1],'L':[0,1],'E':[0,1],'A':[0,1],'F':[1,0],'T':[0,1],'N':[0.5,0.5]}
  

  for root, dir, files in os.walk(path):

    rootpath = os.path.join(os.path.abspath(path), root)

    for file in files:
      if os.path.splitext(file)[1].lower()=='.wav':
        filepath = os.path.join(rootpath, file)

        SR, X = wavfile.read(filepath)

        _, _, spec = mfcc(X, fs=SR, nfft=(nfft*2))

        list_data = numpy.append(list_data, numpy.mean(spec, axis=0)[:nfft]/numpy.max(spec))
        list_label = numpy.append(list_label, dic[file[5]])

  list_data = numpy.reshape(list_data, (len(list_data)/nfft, nfft))
  list_label = numpy.reshape(list_label, (len(list_label)/label_length, label_length))

  return list_data, list_label

def sparkFeatureExt(line):
        print line
        string1= file
        cut=string1[72:]
        cut=cut[:-10]
        intClass=ClassToInt(cut)

        (samplerate, wavedata) = wavfile.read(file)
        (s1,n1)= spectral_centroid(wavedata,512,samplerate)
        (sr1,nr1)= spectral_rolloff(wavedata,512,samplerate)
        (sf1,nf1)= spectral_flux(wavedata,512,samplerate)
        (rms,ts) = root_mean_square(wavedata, 512, samplerate);
        rms= rms[~np.isnan(rms)] #rms array contains NAN values and we have to remove these values
        (zcr,ts1) = zero_crossing_rate(wavedata, 512, samplerate);
        (MFCCs, mspec, spec) = mfcc(wavedata)
        MFCC_coef=list()
        ran=MFCCs.shape
        ran1=ran[0]
        for ind1 in range(13):
            sum=0
            summ=0
            for ind in range(ran1):
                sum+=MFCCs[ind,ind1]
            MFCC_coef.append(sum/ran1)
        eng= stEnergy(wavedata)
        #Win = 0.050
        #Step = 0.050
        #eps = 0.00000001
        return s1,sr1,sf1,rms,zcr,eng,MFCC_coef,intClass

def create_ceps(fn):

    #print(">>>: [%s]" % fn)
    sample_rate, X = scipy.io.wavfile.read(fn)
    #print(">    - [%d]" % sample_rate)
    ceps, mspec, spec = mfcc(X,nceps=13)
    write_ceps(ceps, fn)

    def apply(self, data, meta=None):
        all_ceps = []
        for ch in data:
            ceps, mspec, spec = mfcc(ch)
            all_ceps.append(ceps.ravel())

        return to_np_array(all_ceps)

def mfccIFY(dict_read):
    dict = {}
    for each in dict_read.keys():
        [sample_rate,X] = dict_read.get(each)
        ceps, mspec, spec = mfcc(X)
        dict[each] = ceps
    return dict

def compute_features(source, features):
    """
        compute features for all the tracks
    """
    for label in source.keys():
        for i in range(0,100):
            base_path = os.path.join(FT_DIR, "%s_%d" % (label, i))
            ft = []
            if 'zcr' in features:
                zcr, ts = zero_crossing_rate(source[label][i]['wavedata'], 512, source[label][i]['sample_rate'])
                ft.append(zcr)

            if 'rms' in features:
                rms, ts = root_mean_square(source[label][i]['wavedata'], 512, source[label][i]['sample_rate'])
                ft.append(rms)

            if 'sc' in features:
                sc, ts = spectual_centroid(source[label][i]['wavedata'], 2048, source[label][i]['sample_rate'])
                ft.append(sc)

            if 'sr' in features:
                sr, ts = spectral_rolloff(source[label][i]['wavedata'], 2048, source[label][i]['sample_rate'])
                ft.append(sr)

            if 'sf' in features:
                sf, ts = spectral_flux(source[label][i]['wavedata'], 2048, source[label][i]['sample_rate'])
                ft.append(sf)

            if 'mfcc' in features:
                ceps, mspec, spec = mfcc(source[label][i]['wavedata'])
                ft.append(ceps)

            write_features(ft, base_path)

def convert(path):
	data = {}
	data["sample_rate"], X = scipy.io.wavfile.read(path)
	data["ceps"], data["mspec"], data["spec"] = mfcc(X) #save everything it gives us in case it's useful lmao

	cep_count = len(data["ceps"])
	input_vector = np.array([np.mean(data["ceps"][int(cep_count / 10):int(cep_count * 9 / 10)], axis=0)])
	return input_vector

def create_ceps(fn):
    """
        Creates the MFCC features. 
    """    
    sample_rate, X = scipy.io.wavfile.read(fn)
    X[X==0]=1
    ceps, mspec, spec = mfcc(X)
    write_ceps(ceps, fn)

 def mfcc_sound_features(self):
     ceps, mspec, spec = mfcc(self.audio)
     
     num_ceps = len(ceps)
     #v = np.mean(ceps[int(num_ceps / 10):int(num_ceps * 9 / 10)], axis=0)
     v = np.mean(ceps, axis=0)
     
     return v

def create_ceps(wavfile):
	sampling_rate, song_array = scipy.io.wavfile.read(wavfile)
	"""Get MFCC
	ceps  : ndarray of MFCC
	mspec : ndarray of log-spectrum in the mel-domain
	spec  : spectrum magnitude
	"""
	ceps, mspec, spec = mfcc(song_array)
	write_ceps(ceps, wavfile)

 def show(self, example):
   sound = audiolab.sndfile(self.base + example.file)
   frames = sound.read_frames(sound.get_nframes()) * 0.8
   mfcc = features.mfcc(frames[example.start: example.stop:2], fs=41000)
   print mfcc[0].shape
   fig = plt.figure()
   fig.set_size_inches(20, 20)
   ax = fig.add_subplot(111)
   ax.imshow(mfcc[0].transpose()[:, :100])