예제 #1
0
def high_demo(snd, start, stop):

    freq = linspace(start, stop, len(snd))
    normal_freq = 2 * freq / fs
    highpass_y = aphighpass(snd, normal_freq)

    wavwrite(highpass_y, "aphigh_demo.wav", fs)
예제 #2
0
 def write_aubio_onsets(self, onset_list, filepath):
     print ("Onsets are :%s" % onset_list)
     audio = MonoLoader(filename=filepath)()
     marker = AudioOnsetsMarker(onsets = onset_list, type = 'beep')
     marked_audio = marker(audio)
     wavwrite(marked_audio, switch_ext(os.path.basename(filepath), \
         'AUBIOONSETS.wav'), 44100)
예제 #3
0
def synth_audio(audiofile,
                impfile,
                chns,
                angle,
                nsfile=None,
                snrlevel=None,
                outname=None,
                outsplit=False):
    FreqSamp, audio = wavfile.read(audiofile)
    audio = audio.astype(np.float32) / np.amax(
        np.absolute(audio.astype(np.float32)))
    gen_audio = np.zeros((audio.shape[0], chns), dtype=np.float32)
    for ch in range(1, chns + 1):
        impulse = np.fromfile('{}D{:03d}_ch{}.flt'.format(impfile, angle, ch),
                              dtype=np.float32)
        gen_audio[:, ch - 1] = np.convolve(audio, impulse, mode='same')

    gen_audio = add_noise(gen_audio, nsfile=nsfile, snrlevel=snrlevel)

    if outname is None:
        return FreqSamp, np.transpose(gen_audio)
    if outsplit:
        for ch in range(chns):
            play_data = audiolab.wavwrite(gen_audio[:, ch],
                                          '{}_ch{:02d}.wav'.format(
                                              outname, ch),
                                          fs=FreqSamp,
                                          enc='pcm16')
        return
    else:
        play_data = audiolab.wavwrite(gen_audio,
                                      '{}.wav'.format(outname),
                                      fs=FreqSamp,
                                      enc='pcm16')
    return
예제 #4
0
def to_audio(wav_file, index_file):
    audio_snippets = {}
    for dtype, inverted_vocab in get_inverse_vocabs().iteritems():
        if dtype == RHYTHM_GROUP:
            continue
        elif dtype == CHROMA_GROUP:
            inverted_vocab = scale(inverted_vocab)
            inverted_vocab[inverted_vocab < 0.0] = 0.0
        for i, vec in enumerate(inverted_vocab):
            token = dtype + str(i)
            if dtype == CHROMA_GROUP:
                audio_snippets[token] = gen_chroma(vec)
            elif dtype == TIMBRE_GROUP:
                audio_snippets[token] = gen_timbre(vec)
    keys = audio_snippets.keys()
    index = {}
    for i, key in enumerate(keys):
        t = i * audio_time
        end_t = (i+1) * audio_time
        index[key] = [t, end_t]
    with open(index_file, 'wb') as f:
        json.dump(index, f)
    wavwrite(np.hstack([audio_snippets[key] for key in keys]),
             wav_file,
             fs=44100)
def record(output, cfilename = 'SpikeTrain2Play.wav', fs=44100, enc = 'pcm26'):
    """ record the 'sound' produced by a neuron. Takes a spike train as the
    output.

    >>> record(my_spike_train)

    """


    # from the spike list
    simtime_seconds = (output.t_stop - output.t_start)/1000.
    #time = numpy.linspace(0, simtime_seconds , fs*simtime_seconds)
    (trace,time) = numpy.histogram(output.spike_times*1000., fs*simtime_seconds)


    # TODO convolve with proper spike...
    spike = numpy.ones((fs/1000.,)) # one ms

    trace = numpy.convolve(trace, spike, mode='same')#/2.0
    trace /= numpy.abs(trace).max() * 1.1

    try:
        from scikits.audiolab import wavwrite
    except ImportError:
        print "You need the scikits.audiolab package to produce sounds !"
    wavwrite(trace, cfilename, fs = fs, enc = enc)
예제 #6
0
def main():
    """
    Main function for processing the specified soundfile through this reverb.
    """

    parser = argparse.ArgumentParser(description='Artificial Reverb')
    parser.add_argument('soundfile', help='audio file to process', type=validInput)        # the soundfile is the first agument, with parameter values to follow
    parser.add_argument('outfile', help='path to output file', type=validInput)
    parser.add_argument('-w', '--wetdry', default=0.2, type=float, help='amount of wet signal in the mix')
    parser.add_argument('-da', '--damping', default=0.25, type=float, help='amount of high frequency damping')
    parser.add_argument('-de', '--decay', default=0.4, type=float, help='amount of attentuation applied to signal to make it decay')
    parser.add_argument('-pd', '--predelay', default=30, type=float, help='amount of time before starting reverb')
    parser.add_argument('-b', '--bandwidth', default=0.6, type=float, help='amount of high frequency attentuation on input')
    parser.add_argument('-t', '--tankoffset', default=0, type=float, help='amount of time (ms) to increase the last tank delay time')

    # Parse the commandline arguments
    args = parser.parse_args()

    # Get the entire path and assign soundfile
    soundfilePath = os.path.join(os.getcwd(), args.soundfile)
    
    # From here on, x refers to the input signal
    x, sampleRate, wavType = wavread(soundfilePath)
    dry = x.copy()

    y = reverbTest(x, sampleRate, args.damping, args.decay, args.predelay, args.bandwidth, args.tankoffset)

    # Apply wet/dry mix
    output = dryWet(dry, y, args.wetdry)

    # Finally write the output file
    wavwrite(transpose(output), args.outfile, sampleRate)
예제 #7
0
def wavwrite(srcfile, fs, training):
    try:
        mat = io.loadmat(srcfile)
    except ValueError:
        print('Could not load %s' % srcfile)
        return

    dat = mat['dataStruct'][0, 0][0]
    if ds_factor != 1:
        dat = signal.decimate(dat, ds_factor, axis=0, zero_phase=True)

    mn = dat.min()
    mx = dat.max()
    mx = float(max(abs(mx), abs(mn)))
    if training and mx == 0:
        print('skipping %s' % srcfile)
        return
    if mx != 0:
        dat *= 0x7FFF / mx
    dat = np.int16(dat)

    winsize = win_dur * 60 * fs
    stride = 60 * fs
    for elec in range(16):
        aud = dat[:, elec]
        for win in range(nwin):
            dstfile = srcfile.replace('mat',
                                      str(win) + '.' + str(elec) + '.wav')
            beg = win * stride
            end = beg + winsize
            clip = aud[beg:end]
            audiolab.wavwrite(clip, dstfile, fs=fs, enc='pcm16')
예제 #8
0
def high_demo(snd, start, stop):

    freq = linspace(start, stop, len(snd))
    normal_freq = 2 * freq / fs
    highpass_y = aphighpass(snd, normal_freq)

    wavwrite(highpass_y, "aphigh_demo.wav", fs)
예제 #9
0
def process(file):
    # read in the file
    f, sr, enc = wavread(file)
    # compute the fourier transform & compute the window times:
    D = librosa.stft(f)
    times = librosa.frames_to_samples(np.arange(D.shape[1]))
    # compute the onset strength envelope:
    env = librosa.onset.onset_strength(y=f, sr=sr)
    assert (len(times) == len(env))
    # compute the onsets we are actually interested in, convert to samples:
    onsets = librosa.onset.onset_detect(y=f, sr=sr)
    onset_samps = librosa.frames_to_samples(onsets)
    assert (onset_samps[-1] <= len(f))
    # create a lookup table for retrieving onset strenghts:
    lookup = []
    prevval = 0
    for v in onset_samps:
        for i in xrange(prevval, len(times)):
            if times[i] == v:
                lookup.append(i)
                prevval = i + 1
                break
    # create an empty audio buffer (result):
    result = np.zeros(len(f))
    # write envelope onset strength values at every onset point
    # computed by the envelope:
    for i in xrange(len(lookup)):
        result[onset_samps[i]] = env[lookup[i]]
    # write the result:
    wavwrite(result, file[:-4] + '_proc.wav', sr, enc)
    return
예제 #10
0
    def new_numeral_captcha_on_words(self, fname):
        wordstr = wordstrgen.get_random_wordstr(self.wordbank, self.nwords)
        numstr = wordstrgen.get_random_numstr(self.nnums)

        ensure_dir('temp')
        #these are the filenames of the audio files
        wordaudio = speechsynth.make_audio(wordstr, 'words', './temp/')
        numaudio = speechsynth.make_audio(numstr, 'nums', './temp/')

        # read audio data
        wordaudio_data, fs_word, enc_word = wavread(wordaudio)
        numaudio_data, fs_num, enc_num = wavread(numaudio)

        wordaudio_data = ensure_equal_length(wordaudio_data, numaudio_data)

        # combine audio data modifying volumes
        captcha_audio = self.noise_vol * wordaudio_data + self.captcha_vol * numaudio_data

        outputfname = self.outputdir + fname

        if (os.path.exists(outputfname)): os.remove(outputfname)

        wavwrite(captcha_audio, outputfname, 22050)

        # return output filename and the answer
        return outputfname, prettify(numstr)
예제 #11
0
def sibilant_detector(filename):
    """
	The aim of this algorithm is to detect where are the parts in filename where the energy is maximal.
	This algorithm works as follows:
	1- First compute the spectrogram
	2- Then compute a gaussian curve centered in the frequency researched. Usually for sibilants it's around 6000 Hz
	3- Multiply the spectrum and the gaussian in order to weight the spectrum
	4- Mean all the resultant signal and normalize
	5- The peaks in the resulting signal are the parts in time where the energy in the researched area is the most important.
	"""
    sound_data, fs, enc = wavread(filename)

    #Gaussian coefs
    sigma = 5
    mu = 10000  # mean frequency
    NFFT = 512

    #Spectre
    Pxx, freqs, bins, im = specgram(sound_data, NFFT=NFFT, noverlap=128, Fs=fs)
    show()

    #Siflantes detector
    nb_of_windows = Pxx.shape[1]
    nb_of_fft_coefs = Pxx.shape[0]

    #Compute the gaussian vector and plot
    weights = weighting_vector(nb_of_fft_coefs, sigma, mu, fs)
    f_wweights = np.linspace(0, fs / 2, len(weights), endpoint=True)
    plot(f_wweights, weights)
    show()

    fft_coeficients = np.zeros(nb_of_fft_coefs)
    sibilant_desc = []
    weighted_ffts = []

    #Multiply the weights and the spectrum and show the multiplication
    for i in range(nb_of_windows):
        weighted_fft = Pxx[:, i] * weights

        if len(weighted_ffts) == 0:
            weighted_ffts = weighted_fft
        else:
            weighted_ffts = np.c_[weighted_ffts, weighted_fft]

        sibilant_desc.append(sum(weighted_fft))

    imshow(weighted_ffts, interpolation='nearest', aspect='auto')
    show()

    #Now mean the matrix to have only one descriptor
    sibilant_desc = [float(i) / max(sibilant_desc) for i in sibilant_desc]
    plot(sibilant_desc)
    show()

    #export audio
    max_index, max_value = max(enumerate(sibilant_desc),
                               key=operator.itemgetter(1))
    wavwrite(sound_data[(max_index - 5) * NFFT:(max_index + 5) * NFFT],
             'test.wav',
             fs=44100)
예제 #12
0
def low_demo(snd, start, stop):
    # lowpass at starting at 100 and ending at 1000

    freq = linspace(start, stop, len(snd))
    normal_freq = 2 * freq / fs
    lowpass_y = aplowpass(snd, normal_freq)

    wavwrite(lowpass_y, "aplow_demo.wav", fs)
예제 #13
0
def envelopefile(file, attack=1, release=10):
    # read in the file:
    f, sr, enc = wavread(file)
    env = Envelope()
    env.configure(attackTime=attack, releaseTime=release)
    result = env(essentia.array(f))
    wavwrite(result, file[:-4] + '_env.wav', sr, enc)
    return
예제 #14
0
def low_demo(snd, start, stop):
    # lowpass at starting at 100 and ending at 1000

    freq = linspace(start, stop, len(snd))
    normal_freq = 2 * freq / fs
    lowpass_y = aplowpass(snd, normal_freq)

    wavwrite(lowpass_y, "aplow_demo.wav", fs)
예제 #15
0
def ogg_to_wav(source, target):
    """
	source : source audio file
	target : target audio file
	"""
    x, fs, enc = oggread(source)
    WavFileName = target
    wavwrite(x, WavFileName, fs, enc='pcm24')
예제 #16
0
def wav_to_aif(source, target):
    """
	source : fsource audio file
	target : starget audio file
	"""
    x, fs, enc = wavread(str(file))
    AifFileName = target
    wavwrite(x, AifFileName, fs, enc='pcm24')
예제 #17
0
 def persist(self, filepath=None):
     """   
         Saves the mosaic to that location on disk indicated by
         the `filepath` parameter. 
         
     """
     if filepath:
         self.filepath = filepath
     wavwrite(self.data, self.filepath, self.sample_rate)
예제 #18
0
파일: add_bounds.py 프로젝트: beckgom/msaf
def add_boundaries(wavfile, boundaries, output='output.wav',
                   boundsound="sounds/bell.wav", start=0, end=None):
    """Adds a cowbell sound for each boundary and saves it into a new wav file.

        @param wavfile string: Input wav file (sampled at 11025Hz or 44100Hz).
        @param boundaries np.array: Set of times representing the boundaries
            (in seconds).
        @param output string: Name of the output wav file.
        @param boundsound string: Sound to add to the original file.
        @param start float: Start time (in seconds)
        @param end float: End time (in seconds)

    """

    OFFSET = 0.0  # offset time in seconds

    x, fs = read_wav(wavfile)
    xb, fsb = read_wav(boundsound)

    # Normalize
    x /= x.max()

    # Copy the input wav file to the output
    out = np.zeros(x.size + xb.size + 1000)
    out[:x.size] = x / 3.0

    # Add boundaries
    for bound in boundaries:
        start_idx = int((bound + OFFSET) * fs)
        end_idx = start_idx + xb.size
        read_frames = out[start_idx:end_idx].size
        out[start_idx:end_idx] += xb[:read_frames]

    # Cut track if needed
    start_time = start * fs
    if start_time < 0:
        start_time = 0
    if end is None:
        end_time = len(out)
    else:
        end_time = end * fs
        if end_time > len(out):
            end_time = len(out)

    out = out[int(start_time):int(end_time)]

    # Write output wav
    audiolab.wavwrite(out, output, fs=fs)

    # Convert to MP3 and delete wav
    dest_mp3 = output.replace(".wav", ".mp3")
    wav2mp3(output, dest_mp3)
    os.remove(output)

    print "Wrote %s" % dest_mp3
예제 #19
0
def mono_to_stereo(source, target):
    import numpy as np
    f = Sndfile(source, 'r')
    if f.channels == 1:

        #To mono
        x, fs, enc = wavread(source)
        print "here"
        print type(x)
        print type(np.array([x, x]))
        wavwrite(np.array([x, x]).transpose(), target, fs, enc='pcm24')
예제 #20
0
def wav_to_mono(source, target):
    """
	source : source audio file
	target : target audio file
	"""
    f = Sndfile(source, 'r')
    if f.channels != 1:
        #To mono
        x, fs, enc = wavread(source)
        f.channels
        wavwrite(x[:, 0], target, fs, enc='pcm24')
예제 #21
0
def aif_to_wav(source, target):
    """
	source : source audio file
	target : target audio file
	"""
    try:
        x, fs, enc = aiffread(str(source))
        WavFileName = target
        wavwrite(x, WavFileName, fs, enc='pcm24')
    except:
        print "File is not aif"
        pass
예제 #22
0
def cut_silence_in_sound(source, target, rmsTreshhold=-40, WndSize=128):
    """
	source : fsource audio file
	target : output sound
	This function cuts the silence at the begining and at the end of an audio file in order. 
	It's usefull for normalizing the length of the audio stimuli in an experiment.
	The default parameters were tested with notmal speech.
	"""
    NbofWrittendFiles = 1
    x, fs, enc = wavread(str(source))
    index = 0

    #Remove the silence at the begining
    while index + WndSize < len(x):
        DataArray = x[index:index + WndSize]
        rms = np.sqrt(np.mean(np.absolute(DataArray)**2))
        rms = lin2db(rms)
        index = 0.5 * WndSize + index

        if rms > rmsTreshhold:
            end = 0
            beginning = index
            print beginning / 44100
            break

    #Remove the silence at the end
    x, fs, enc = wavread(str(source))
    WndSize = 128
    index = 0
    x = list(reversed(x))

    while index + WndSize < len(x):
        DataArray = x[int(index):int(index + WndSize)]
        rms = np.sqrt(np.mean(np.absolute(DataArray)**2))
        rms = lin2db(rms)
        index = 0.5 * WndSize + index

        if rms > rmsTreshhold:
            end = 0
            final = index
            print(len(x) - final) / 44100
            break

    #write the sound source without silences
    x, fs, enc = wavread(str(source))
    WndSize = 128
    rmsTreshhold = -70
    index = 0

    name_of_source = str(os.path.basename(source))
    name_of_source = os.path.splitext(name_of_source)[0]
    path, sourcename = os.path.split(source)
    wavwrite(x[beginning:len(x) - final], target, fs, enc='pcm24')
예제 #23
0
def generate_silence_sound(duration, fs, name, enc="pcm16"):
    """
	duration : in seconds
	fs       : sampling frequency
	name     : file name to generate
	enc      :  pcm16, pcm24 ...
	"""

    import numpy as np
    from scikits.audiolab import wavwrite
    data = np.zeros(duration * fs)
    wavwrite(data, name, fs, enc)
예제 #24
0
def wavwrite(frames, outfile, rate, enc):
	log.debug('Writing file %s',outfile)
	temp = None
	if outfile.endswith('.mp3'):
		temp = tempfile.mktemp('.wav','b')
		audiolab.wavwrite(frames.compressed(), temp, rate, enc)
		args = ['ffmpeg','-y','-i',temp,outfile]
		log.debug('Calling ffmpeg: %s',args)
		subprocess.call(args,stderr=subprocess.PIPE)
		os.unlink(temp)
	else:
		audiolab.wavwrite(frames.compressed(), outfile, rate, enc)
예제 #25
0
def match_dir(props, prop, outdir, ext):
    ref_prop = min(props.keys(), key=lambda x: props[x][prop])
    ref_peak = max(props.keys(),
                   key=lambda x:
                   (props[x]['peak'] * props[ref_prop][prop] / props[x][prop]))
    for f in props:
        a = props[f]['sig'] * (props[ref_prop][prop] /
                               (props[f][prop] * props[ref_peak]['peak']))
        bname = os.path.basename(f)
        wavwrite(
            a, os.path.join(outdir,
                            os.path.splitext(bname)[0] + ext + '.wav'),
            props[f]['fs'], props[f]['enc'])
예제 #26
0
	def __init__(self, start, end, frames, save_soundfile=False, secs_per_block=2):
		self.start = start
		self.end = end
		self.frames = frames

		tmpwav = tempfile.mktemp('.wav')
		self.soundfile = tmpwav
		audiolab.wavwrite(self.frames, tmpwav, 44100, 'pcm16')
		log.debug('Calculating butterscotch')
		self.signature = audioprocessing.butterscotch(tmpwav, secs_per_block=secs_per_block)
		log.debug('Done')
		if not save_soundfile:
			os.unlink(tmpwav)
예제 #27
0
def execute_flac_convert():
    """
    Cycles through test_data, converting all flac to wav
    Script includes a utility remove spaces and problem 
    characters from file name 
    """
    files = [f for f in glob('*.flac')]

    for af in files:
        x = flacread(af)[0]
        log.debug("Found a flac file: '%s'" % af)
        n = switch_ext(strip_all(af), '.wav')
        print ("Converting '%s' to: '%s'" % (af, n))
        wavwrite(x, n, 44100)
예제 #28
0
def match_dir(props, prop, outdir, ext):
    ref_prop = min(props.keys(), key=lambda x: props[x][prop])
    ref_peak = max(props.keys(),
                   key=lambda x: (props[x]['peak'] *
                                  props[ref_prop][prop] / props[x][prop]))
    for f in props:
        a = props[f]['sig'] * (props[ref_prop][prop] /
                               (props[f][prop] *
                                props[ref_peak]['peak']))
        bname = os.path.basename(f)
        wavwrite(a,
                 os.path.join(outdir,
                              os.path.splitext(bname)[0] + ext + '.wav'),
                 props[f]['fs'],
                 props[f]['enc'])
예제 #29
0
def IndexFileInFolder(FolderName):
    for file in glob.glob(FolderName + "/*.wav"):  # Wav Files
        x, fs, enc = aiffread(str(file))
        WndSize = 16384
        rmsTreshhold = -50

        index = 0
        NbofWrittendFiles = 1
        while index + WndSize < len(x):
            DataArray = x[index:index + WndSize]
            rms = np.sqrt(np.mean(np.absolute(DataArray)**2))
            rms = Lin2db(rms)
            index = WndSize + index
            if rms > -55:
                end = 0
                begining = index
                index = WndSize + index
                while rms > -55:
                    if index + WndSize < len(x):
                        index = WndSize + index
                        DataArray = x[index:index + WndSize]
                        rms = np.sqrt(np.mean(np.absolute(DataArray)**2))
                        rms = Lin2db(rms)
                        end = index
                    else:
                        break

            #if file is over 500 ms long, write it
                if (end - begining) > (fs / 2):
                    duree = (end - begining) / float(fs)
                    print "duree  :  " + str(duree)

                    begining = begining - WndSize
                    if begining < 0:
                        begining = 0

                    end = end + WndSize
                    if end > len(x):
                        end = len(x)

                    name = os.path.splitext(str(file))[0]
                    name = os.path.basename(name)
                    wavwrite(x[begining:end],
                             "Indexed/" + "/" + FolderName + "/" + name + "_" +
                             str(NbofWrittendFiles) + ".wav",
                             fs,
                             enc='pcm24')
                    NbofWrittendFiles = NbofWrittendFiles + 1
예제 #30
0
def clicktrack(file):
    # read in the file:
    f, sr, enc = wavread(file)
    env = Envelope()
    env.configure(attackTime=0, releaseTime=5)
    curve = env(essentia.array(f))
    result = np.zeros(len(curve))
    i = 1
    while i < len(curve):
        if curve[i] - curve[i - 1] > 0.05:
            result[i] = curve[i]  # record the click at the onset
            i += 1100  # advance the playhead by 1100 samples (~22 ms) to avoid closely-spaced clicks
            continue
        i += 1
    wavwrite(result, file[:-4] + '_clicks.wav', sr, enc)
    return
예제 #31
0
def makeTransients(vox, prefix):
    wavwrite(transients(v, trans, 100), prefix + v[-9:-4] + '_transLong.wav',
             44100, 'pcm24')
    wavwrite(transients(v, trans, 20), prefix + v[-9:-4] + '_transShort.wav',
             44100, 'pcm24')
    wavwrite(transients(v, bulbs, 100), prefix + v[-9:-4] + '_bulbs.wav',
             44100, 'pcm24')
    wavwrite(transients(v, tiny, 100), prefix + v[-9:-4] + '_tiny.wav', 44100,
             'pcm24')
예제 #32
0
def normalize_target_audio(input_file='moviehires_endpos_beta02.imatsh.wav', 
                           sources_expr='/home/mkc/Music/GoldbergVariations/*48_1.wav', write_me=False, amp_factor=0.5, proc_audio=True):
    """
    Per-variation normalization of concatenated imatsh file using individual sources as locators
    Assumes that the input_file and the source_dir have the same sample rate
    inputs:
        input_file  - the file to be processed (locally normalized)
        sources_expr- regular expression for input files
        write_me    - write output files when true [False]
        amp_factor  - amplitude change factor (proportion of full scale normalization) [0.5]
        proc_audio  - whether to process target audio using source audio info [1]
    outputs:
        sample_locators - sample locators for each variation
        audio_summaries - min, max, rms values for each variation        
    output files:
        output_file = {input_file_stem}+'norm.'+{input_ext}
    """
    # Compute min, max, rms per source file
    flist = glob.glob(sources_expr)
    flist.sort()
    sample_locators = [0]
    audio_summaries = []
    ext_pos = input_file.rindex('.')
    outfile_stem, ext = input_file[:ext_pos], input_file[ext_pos+1:]
    for i,f in enumerate(flist):
        x,sr,fmt = skaud.wavread(f)
        print f, sr, fmt
        if(len(x.shape)>1):
            x = x[:,0] # Take left-channel only
        sample_locators.extend([len(x)])
        audio_summaries.append([max(abs(x)), np.sqrt(np.mean(x**2))])
        if proc_audio:
            y,sr_y,fmt_y = skaud.wavread(input_file, first=np.cumsum(sample_locators)[-2], last=sample_locators[-1])
            if sr != sr_y:
                raise ValueError("input and source sample rates don't match: %d,%d"%(sr,sr_y))
            audio_summaries.append([max(abs(y[:,0])), np.sqrt(np.mean(y[:,0]**2))])
            max_val = audio_summaries[-1][0]
            rms_val = audio_summaries[-1][1]
            norm_cf = amp_factor / max_val + (1 - amp_factor)
            outfile = outfile_stem+'_%02d.%s'%(i+1,ext)
            max_amp_val = norm_cf * max_val
            rms_amp_val = norm_cf * rms_val
            print '%s: nrm=%05.2fdB, peak=%05.2fdB, *peak=%05.2fdB, rms=%05.2fdB, *rms=%05.2fdB'%(
                outfile, dB(norm_cf), dB(max_val), dB(max_amp_val), dB(rms_val), dB(rms_amp_val))
            if(write_me):
                skaud.wavwrite(norm_cf*y, outfile, sr, fmt)
    return np.cumsum(sample_locators), np.array(audio_summaries)
예제 #33
0
def procFile(v, prefix):
    wavwrite(transients(v, trans, 20), prefix + v[-9:-4] + '_trans00.wav',
             44100, 'pcm24')
    wavwrite(transients(v, trans, 10), prefix + v[-9:-4] + '_trans01.wav',
             44100, 'pcm24')
    wavwrite(resonances(v, reson, 10), prefix + v[-9:-4] + '_reson00.wav',
             44100, 'pcm24')
    wavwrite(resonances(v, reson, 15), prefix + v[-9:-4] + '_reson01.wav',
             44100, 'pcm24')
    return
예제 #34
0
파일: gablab.py 프로젝트: coreyker/gablab
def TestBPDN2():
    # ________________________________________
    print 'Test: basis pursuit decomposition'

    fs = 8000
    btmp = audiolab.wavread('glockenspiel.wav')[0]
    b = samplerate.resample(btmp, fs/44100.,'sinc_best')
    L = len(b)

    A = GaborBlock(L,1024)
    B = GaborBlock(A.M,64)
    C = DictionaryUnion(A,B)
    b = np.hstack((b,np.zeros(C.M-L)))
    
    e = 1e-2
    x = BPDN(C,b,e,100)
    ye = np.real(C.dot(x))
    print 'Error (should be <= %f): %f' % (e,np.sum((b-ye)**2))
    print '----------------------------------------'
    
    xtone = x[:A.N]
    xtrans = x[A.N:]
    ytone = np.real(A.dot(xtone))
    ytrans = np.real(B.dot(xtrans))

    audiolab.wavwrite(ytone,'ytone.wav',fs)
    audiolab.wavwrite(ytrans,'ytrans.wav',fs)

    # tonal decomp
    m = np.log10(np.abs(A.conj().transpose().dot(ytone)))
    tfgrid = np.reshape(range(0,A.N),(A.N/A.fftLen,A.fftLen))
    tfgrid = tfgrid[:,:A.fftLen/2+1]

    pyplot.subplot(2,1,1)
    pyplot.imshow(m[tfgrid].transpose(), aspect='auto', interpolation='bilinear', origin='lower')

    # transient decomp
    m = np.log10(np.abs(B.conj().transpose().dot(ytrans)))
    tfgrid = np.reshape(range(0,B.N),(B.N/B.fftLen,B.fftLen))
    tfgrid = tfgrid[:,:B.fftLen/2+1]

    pyplot.subplot(2,1,2)
    pyplot.imshow(m[tfgrid].transpose(), aspect='auto', interpolation='bilinear', origin='lower')

    pyplot.show()
예제 #35
0
def fastICA(mix_file, jamming_file):
    sig1, fs1, enc1 = wavread(mix_file)
    sig2, fs2, enc2 = wavread(jamming_file)
    sig1, sig2 = chop_sig(sig1, sig2)
    wavwrite(array([sig1, sig2]).T, "mixed.wav", fs1, enc1)
    # Load in the stereo file
    recording, fs, enc = wavread("mixed.wav")

    # Perform FastICA algorithm on the two channels
    sources = fastica(recording)

    # The output levels of this algorithm are arbitrary, so normalize them to 1.0.

    m = []
    for k in sources:
        m.append(k[0])
    # Write back to a file
    wavwrite(array(m), "sources.wav", fs, enc)
예제 #36
0
def trackify(path, filename):
    clips = readfiles(getfiles(path))
    indices = range(len(clips))
    lengths = []
    for c in clips:
        lengths.append(lenSec(c))
    maxFade = min(lengths)
    minFade = maxFade / 3
    shuffle(indices)
    result = crossfade(clips[indices[0]], clips[indices[1]],
                       random.random() * (maxFade - minFade) + minFade)
    for i in xrange(2, len(indices)):
        maxFade = lenSec(clips[indices[i]])
        result = crossfade(
            result, clips[indices[i]],
            random.random() * (.666 * maxFade) + (.333 * maxFade))
    wavwrite(result.data, filename, result.sr, result.enc)
    return
예제 #37
0
def write_file(wave, filename):
    from os import path
    ext = path.splitext(filename)[1].lower()
    if ext == WAV_EXT:
        return audiolab.wavwrite(wave.waveform, filename, fs=wave.samplerate)
    else:
        raise NotImplementedError(
            "Format '%s' not supported. Supported formats are: %s" %
            (ext, ', '.join(SUPPORTED_FORMATS)))
예제 #38
0
def wavwrite(srcfile):
    try:
        mat = io.loadmat(srcfile)
    except ValueError:
        print('Could not load %s' % srcfile)
        return

    dat = mat['dataStruct'][0, 0][0]
    mn = dat.min()
    mx = dat.max()
    mx = float(max(abs(mx), abs(mn)))
    if mx != 0:
        dat *= 0x7FFF / mx
    dat = np.int16(dat)

    for elec in range(16):
        dstfile = srcfile.replace('mat', str(elec) + '.wav')
        aud = dat[:, elec]
        audiolab.wavwrite(aud, dstfile, fs=400, enc='pcm16')
def generateSamples(label, background):
    print 'Saving ' + label
    output_folder = OUTPUT_DIRECTORY + '/' + label + '/'
    input_folder = INPUT_DIRECTORY + '/' + label + '/'

    if not os.path.exists(output_folder):
        os.mkdir(output_folder)
    wavfiles = [input_folder + '/' + wavfile for wavfile in os.listdir(input_folder)]
    print(wavfiles)
    nr = 1
    for file in wavfiles:
        data, fs, _ = wavread(file)
        data_len = len(data)
        for i in range(0, SAMPLES_PER_ORYGINAL):
            start = random.randint(0, len(background) - data_len)
            ratio = 0.25 + random.random() * 0.5 # range 0.25 - 0.75
            sample = (1 - ratio) * data + ratio * background[start:start+data_len]
            print 'Saving ' + label + '/' + str(nr) + '.wav'
            wavwrite(sample, output_folder + str(nr) + '.wav', fs=fs)
            nr += 1
예제 #40
0
파일: audiofile.py 프로젝트: fduch2k/aeneas
    def write(self, file_path):
        """
        Write the audio data to file.
        Return ``True`` on success, or ``False`` otherwise.

        This function works only for mono wav files!

        :param file_path: the path of the output file to be written
        :type  file_path: string (path)
        :rtype: bool

        .. versionadded:: 1.2.0
        """
        self._log(["Writing audio file '%s'...", file_path])
        try:
            wavwrite(self.audio_data, file_path, self.audio_sample_rate, self.audio_format)
        except:
            self._log("Error writing audio file", severity=Logger.CRITICAL)
            return False
        return True
예제 #41
0
def recordAudio():

    CHUNK = 1024
    FORMAT = pyaudio.paInt16
    CHANNELS = 1
    RATE = 44100
    RECORD_SECONDS = 1
    WAVE_OUTPUT_FILENAME = "audioOriginal.wav"

    p = pyaudio.PyAudio()

    stream = p.open(format=FORMAT,
                    channels=CHANNELS,
                    rate=RATE,
                    input=True,
                    frames_per_buffer=CHUNK)

    print("* recording:")

    frames = []

    for i in range(0, int(RATE / CHUNK * RECORD_SECONDS)):
        data = stream.read(CHUNK)
        frames.append(data)

    print("* Finished recording.")

    stream.stop_stream()
    stream.close()
    p.terminate()

    wf = wave.open(WAVE_OUTPUT_FILENAME, 'wb')
    wf.setnchannels(CHANNELS)
    wf.setsampwidth(p.get_sample_size(FORMAT))
    wf.setframerate(RATE)
    wf.writeframes(b''.join(frames))
    wf.close()

    # Duplicate audio and save as Actual
    frames, fs, encoder = audiolab.wavread('audioOriginal.wav')
    audiolab.wavwrite(frames, 'audioActual.wav', fs)
예제 #42
0
def recordAudio():

    CHUNK = 1024
    FORMAT = pyaudio.paInt16
    CHANNELS = 1
    RATE = 44100
    RECORD_SECONDS = 1
    WAVE_OUTPUT_FILENAME = "audioOriginal.wav"

    p = pyaudio.PyAudio()

    stream = p.open(format=FORMAT,
                channels=CHANNELS,
                rate=RATE,
                input=True,
                frames_per_buffer=CHUNK)

    print("* recording:")

    frames = []

    for i in range(0, int(RATE / CHUNK * RECORD_SECONDS)):
        data = stream.read(CHUNK)
        frames.append(data)

    print("* Finished recording.")

    stream.stop_stream()
    stream.close()
    p.terminate()

    wf = wave.open(WAVE_OUTPUT_FILENAME, 'wb')
    wf.setnchannels(CHANNELS)
    wf.setsampwidth(p.get_sample_size(FORMAT))
    wf.setframerate(RATE)
    wf.writeframes(b''.join(frames))
    wf.close()

    # Duplicate audio and save as Actual
    frames, fs, encoder = audiolab.wavread('audioOriginal.wav')
    audiolab.wavwrite(frames,'audioActual.wav',fs)
예제 #43
0
파일: audiofile.py 프로젝트: fduch2k/aeneas
    def write(self, file_path):
        """
        Write the audio data to file.
        Return ``True`` on success, or ``False`` otherwise.

        This function works only for mono wav files!

        :param file_path: the path of the output file to be written
        :type  file_path: string (path)
        :rtype: bool

        .. versionadded:: 1.2.0
        """
        self._log(["Writing audio file '%s'...", file_path])
        try:
            wavwrite(self.audio_data, file_path, self.audio_sample_rate,
                     self.audio_format)
        except:
            self._log("Error writing audio file", severity=Logger.CRITICAL)
            return False
        return True
예제 #44
0
파일: main.py 프로젝트: lsiddd/waveleeeeeet
def main():
    originalArray, noisyArray, fs = fn.noisemixer(-10)
    print 'snr original: ' + str(fn.snrcalculation(originalArray,
                                                   noisyArray)) + ' Db'

    order = 5
    butterworthcutoff = 700  # Hz
    walevetcutoff = 11025

    btt_filtered = fn.butter_lowpass_filter(originalArray + noisyArray,
                                            butterworthcutoff, fs, order)
    print 'butterworth filtered data snr: ' + str(
        fn.snrcalculation(originalArray, originalArray - btt_filtered))

    wavwrite(btt_filtered, 'butterworth_lowpass.wav', fs)

    wv_filtered = fn.wavelet_hard(originalArray + noisyArray, walevetcutoff,
                                  fs, order)
    print 'wavelet_hard filtered data snr: ' + str(
        fn.snrcalculation(originalArray, originalArray - wv_filtered))
    wavwrite(wv_filtered, 'wavelet_hard.wav', fs)
    '''
예제 #45
0
def batchChordGenerate(taskList, prefix):
    '''
    inputs: a list of lists
    each sublist contains the following values @ indices:
        0 : vox collection index
        1 : vox sample index
        2 : a list: [sample #, transposition semitones, transposition cents]
        3 : ... and so on ...
    '''
    for i, t in enumerate(taskList):
        print "begin task for vocal gesture " + p(t[0]) + '_' + p(t[1])
        # grab the vocal gesture:
        gestur = vox[t[0]][t[1]]
        # compute clicks to be applied in all files:
        idx, l = clicks(gestur)
        clk = [idx, l]
        # compute envelope to be applied to all files:
        env = envelope(gestur, 10, 30)
        env *= (1 / env.max())  # normalize envelope
        # proceed with computing:
        fp = prefix + '_' + p(i) + '__' + p(t[0]) + '_' + p(t[1]) + '__'
        sr = None
        enc = None
        for j in xrange(2, len(t)):
            fname = fp + str(j - 2) + '__' + str(t[j][0]) + '.wav'
            print "    computing " + str(j) + ' : ' + fname
            transpratio = tr(t[j][1], t[j][2])
            # print "    transposition : " + str(t[j][1]) + ' ' + str(t[j][2]) + ' = ' + str(transpratio)
            rezs = []  # array of np.arrays containing transposed resonances
            for f in coll_safe[t[j][0]]:
                # print "         file : " + f
                x, sr, enc = wavread(f)
                rezs.append(resample(x, transpratio, 'sinc_best'))
            wavwrite(resonancesChord(None, rezs, 30, clk, env), fname, 44100,
                     'pcm24')
    return
예제 #46
0
from mdp import fastica
from scikits.audiolab import wavread, wavwrite
from numpy import abs, max
 
# Load in the stereo file

#for i in range(0, 1500):
#	recording, fs, enc = wavread('piano/wav/' + str(i) + '.wav')
#	recording /= (5 * max(abs(recording), axis = 0))
#	wavwrite(recording, 'piano/wav/' + str(i) + '.wav', fs, enc)
	
#for i in range(0, 1500):
#	recording, fs, enc = wavread('clarinet/wav/' + str(i) + '.wav')
#	recording /= (5 * max(abs(recording), axis = 0))
#	wavwrite(recording, 'clarinet/wav/' + str(i) + '.wav', fs, enc)

for i in range(0, 6000):
	recording, fs, enc = wavread('wav/' + str(i) + '.wav')
	recording /= (5 * max(abs(recording), axis = 0))
	wavwrite(recording, 'wav/' + str(i) + '.wav', fs, enc)
예제 #47
0
    def synthesize(self, text_file, audio_file_path, quit_after=None, backwards=False):
        """
        Synthesize the text contained in the given fragment list
        into a ``wav`` file.

        :param text_file: the text file to be synthesized
        :type  text_file: :class:`aeneas.textfile.TextFile`
        :param audio_file_path: the path to the output audio file
        :type  audio_file_path: string (path)
        :param quit_after: stop synthesizing as soon as
                           reaching this many seconds
        :type  quit_after: float
        :param backwards: synthesizing from the end of the text file
        :type  backwards: bool
        """

        # time anchors
        anchors = []

        # initialize time
        current_time = 0.0

        # waves is used to concatenate all the fragments WAV files
        waves = numpy.array([])

        # espeak wrapper
        espeak = ESPEAKWrapper(logger=self.logger)

        if quit_after is not None:
            self._log(["Quit after reaching %.3f", quit_after])
        if backwards:
            self._log("Synthesizing backwards")

        # for each fragment, synthesize it and concatenate it
        num = 0
        num_chars = 0
        fragments = text_file.fragments
        if backwards:
            fragments = fragments[::-1]
        for fragment in fragments:

            # synthesize and get the duration of the output file
            self._log(["Synthesizing fragment %d", num])
            handler, tmp_destination = tempfile.mkstemp(
                suffix=".wav",
                dir=gf.custom_tmp_dir()
            )
            duration = espeak.synthesize(
                text=fragment.text,
                language=fragment.language,
                output_file_path=tmp_destination
            )

            # store for later output
            anchors.append([current_time, fragment.identifier, fragment.text])

            # increase the character counter
            num_chars += fragment.characters

            # concatenate to buffer
            self._log(["Fragment %d starts at: %f", num, current_time])
            if duration > 0:
                self._log(["Fragment %d duration: %f", num, duration])
                current_time += duration
                data, sample_frequency, encoding = wavread(tmp_destination)
                #
                # TODO this might result in memory swapping
                # if we have a large number of fragments
                # is there a better way?
                #
                # NOTE since append cannot be in place,
                # it seems that the only alternative is pre-allocating
                # the destination array,
                # possibly truncating or extending it as needed
                #
                if backwards:
                    waves = numpy.append(data, waves)
                else:
                    waves = numpy.append(waves, data)
            else:
                self._log(["Fragment %d has zero duration", num])

            # remove temporary file
            self._log(["Removing temporary file '%s'", tmp_destination])
            os.close(handler)
            os.remove(tmp_destination)
            num += 1

            if (quit_after is not None) and (current_time > quit_after):
                self._log(["Quitting after reached duration %.3f", current_time])
                break

        # output WAV file, concatenation of synthesized fragments
        self._log(["Writing audio file '%s'", audio_file_path])
        wavwrite(waves, audio_file_path, sample_frequency, encoding)

        # return the time anchors
        # TODO anchors do not make sense if backwards == True
        self._log(["Returning %d time anchors", len(anchors)])
        self._log(["Current time %.3f", current_time])
        self._log(["Synthesized %d characters", num_chars])
        return (anchors, current_time, num_chars)
예제 #48
0
from tempfile import mkstemp
from os.path import join, dirname
from os import remove

from scikits.audiolab import wavread, wavwrite

(tmp, fs, enc)  = wavread('test.wav')
if tmp.ndim < 2:
    nc  = 1
else:
    nc  = tmp.shape[1]

print "The file has %d frames, %d channel(s)" % (tmp.shape[0], nc)
print "FS is %f, encoding is %s" % (fs, enc)

fd, cfilename   = mkstemp('pysndfiletest.wav')
try:
    wavwrite(tmp, cfilename, fs = 16000, enc = 'pcm24')
finally:
    remove(cfilename)
예제 #49
0
파일: tremolo.py 프로젝트: ttm/dissertacao
x=n.linspace(0,2*n.pi,Dv,endpoint=False)
tabv=n.sin(x) # tabela senoidal para o tremolo

# Padrao do vibrato
ii=n.arange(fa * dur) # amostras em dur segundos
gv=n.array(ii*fv*float(D)/fa, n.int) # indices para pegar na tabela

### Som em si
tab=n.linspace(-1,1,D) # dente de serra
tv=10**(tab[gv%D]*mu/20) # desvio instantaneo de amplitude para cada amostra

gi=n.array(ii*f*(Dv/float(fa)), n.int) # a movimentacao na tabela total, jah inteiro
ti=tabv[gi%Dv]*tv
p.plot(ti,label=r"$T_i^{tr(f'=1,5Hz)}=\{t_i.a_i\}_0^{\Lambda-1}$", linewidth=2)
ti=((ti-ti.min())/(ti.max()-ti.min()))*2-1 # normalizando
a.wavwrite(ti,"tremolo.wav",fa)

gi=n.array(  ii * (D/float(fa)) * f  , n.int ) % D
t=tab[ gi ]
a.wavwrite(t,"original.wav",fa)

p.ylabel(r"amplitude $\quad \rightarrow $", fontsize=16)
p.xlabel(r"$i\quad \rightarrow$",fontsize=26)

p.xlim(-2000,ii[-1]+2000)
p.ylim(-4.3,6)
p.xticks((0,20000,40000,60000,80000,88200),(r"0",20000,40000,60000,80000,88200))

p.plot(tv, label =r"$a_i=10^{t_i'\,\frac{V_dB=12\,dB}{20}}$", linewidth=4 )
p.legend(loc="upper left")
ltext = p.gca().get_legend().get_texts()
예제 #50
0
파일: ica.py 프로젝트: verngutz/Aura-Mismo
from mdp import fastica
from scikits.audiolab import wavread, wavwrite
from numpy import abs, max
 
# Load in the stereo file
recording, fs, enc = wavread('test.wav')
 
# Perform FastICA algorithm on the two channels
sources = fastica(recording)
 
# The output levels of this algorithm are arbitrary, so normalize them to 1.0.
sources /= (5 * max(abs(sources), axis = 0))
 
# Write back to a file
wavwrite(sources, 'testout.wav', fs, enc)
예제 #51
0
    for d in dirs:
        output_dir = os.path.join(root,d+'_'+mode)
        if not os.path.exists(output_dir):
            os.makedirs(output_dir)

    origFiles = glob.glob(os.path.join(root, 'Original', '*.wav'))
    
    for d in dirs:
        noisyFiles = glob.glob(os.path.join(root, d, '*.wav'))
        for a,b in zip(origFiles,noisyFiles):

            f = os.path.split(b)[1]
            print 'Processing %s' % f
            print '-----------------------------'
            y = audiolab.wavread(a)[0]
            z = audiolab.wavread(b)[0]
            r = y-z
            
            inSNR = 10*np.log10(y.dot(y)/r.dot(r))
            nvar = np.sum(np.abs(y)**2)/(10**(inSNR/10))
            
            ye = Process(z, nvar, mode)
            r = y-ye
            outSNR = 10*np.log10(y.dot(y)/r.dot(r))
        
            print 'File: %s, Input SNR = %f, output SNR = %f' % (f, inSNR, outSNR)
            audiolab.wavwrite(ye,os.path.join(root,d+'_'+mode,'t_'+f),44100.)


예제 #52
0
            pred = np.argmax(np.sum(fprop(X_adv), axis=0))
            if pred == t:
                dnn_file.write('{}\t'.format(int(out_snr+.5)))
            else:
                dnn_file.write('{}\t'.format('na'))

            # aux prediction
            if args.aux_model:
                X_adv_agg = aggregate_features(dnn_model, X_adv, which_layers)
                pred = np.argmax(np.bincount(np.array(aux_model.predict(X_adv_agg), dtype='int')))
                if pred == t:
                    aux_file.write('{}\t'.format(int(out_snr+.5)))
                else:
                    aux_file.write('{}\t'.format('na'))

            # SAVE ADVERSARY FILES
            out_file = os.path.join(args.out_path,
            '{fname}.{label}.adversary.{snr}dB.wav'.format(
                fname=fname,
                label=label_list[t],
                snr=int(out_snr+.5)))
            audiolab.wavwrite(x_adv, out_file, fs)

        dnn_file.write('\n'.format(fname))
        if args.aux_model:
            aux_file.write('\n'.format(fname))
    
    dnn_file.close()
    if args.aux_model:
        aux_file.close()
    
예제 #53
0
파일: synthesizer.py 프로젝트: dburt/aeneas
    def synthesize(self, text_file, audio_file_path):
        """
        Synthesize the text contained in the given fragment list
        into a ``wav`` file.

        :param text_file: the text file to be synthesized
        :type  text_file: :class:`aeneas.textfile.TextFile`
        :param audio_file_path: the path to the output audio file
        :type  audio_file_path: string (path)
        """
        
        # time anchors
        anchors = []

        # initialize time
        current_time = 0.0

        # waves is used to concatenate all the fragments WAV files
        waves = numpy.array([])

        # espeak wrapper
        espeak = ESPEAKWrapper(logger=self.logger)

        num = 0
        # for each fragment, synthesize it and concatenate it
        for fragment in text_file.fragments:

            # synthesize and get the duration of the output file
            self._log("Synthesizing fragment %d" % num)
            handler, tmp_destination = tempfile.mkstemp(
                suffix=".wav",
                dir=gf.custom_tmp_dir()
            )
            duration = espeak.synthesize(
                text=fragment.text,
                language=fragment.language,
                output_file_path=tmp_destination
            )

            # store for later output
            anchors.append([current_time, fragment.identifier, fragment.text])

            # concatenate to buffer
            self._log("Fragment %d starts at: %f" % (num, current_time))
            if duration > 0:
                self._log("Fragment %d duration: %f" % (num, duration))
                current_time += duration
                data, sample_frequency, encoding = wavread(tmp_destination)
                #
                # TODO this might result in memory swapping
                # if we have a large number of fragments
                # is there a better way?
                #
                # waves = numpy.concatenate((waves, data))
                #
                # append seems faster than concatenate, as it should
                waves = numpy.append(waves, data)
            else:
                self._log("Fragment %d has zero duration" % num)

            # remove temporary file
            self._log("Removing temporary file '%s'" % tmp_destination)
            os.close(handler)
            os.remove(tmp_destination)
            num += 1

        # output WAV file, concatenation of synthesized fragments
        self._log("Writing audio file '%s'" % audio_file_path)
        wavwrite(waves, audio_file_path, sample_frequency, encoding)

        # return the time anchors
        self._log("Returning %d time anchors" % len(anchors))
        return anchors
예제 #54
0
파일: ruidos.py 프로젝트: ttm/dissertacao
# Ruido Violeta:
# a cada oitava, ganhamos 6dB
fator=10.**(6/20.)

alphai=fator**(n.log2(fi[i0:]/f0))
c=n.copy(coefs)
c[i0:]=c[i0:]*alphai
# real par, imaginaria impar


c[N/2+1:]=n.real(c[1:N/2])[::-1] - 1j*n.imag(c[1:N/2])[::-1]

ruido=n.fft.ifft(c)
r=n.real(ruido)
r=((r-r.min())/(r.max()-r.min()))*2-1
a.wavwrite(r,'violeta.wav',44100)

p.subplot(521)
p.title(u'ruído violeta')
p.ylim(-10,220)
p.plot(n.log10(fi[i0:len(fi)/2]),20*n.log2(n.abs(c[i0:len(c)/2])))
p.subplot(522)
p.plot(r[ii:ie])
p.plot(r[ii:ie],'ro', markersize=4)

#############
# Ruido Azul

# para cada oitava, ganhamos 3dB
fator=10.**(3/20.)
예제 #55
0
            # aux prediction
            X_adv_agg_filt = aggregate_features(dnn_model, X_adv_filt, which_layers)
            pred = np.argmax(np.bincount(np.array(aux_model.predict(X_adv_agg_filt), dtype='int')))
            if pred == t:
                aux_file_filt.write('{}\t'.format('x'))
            else:
                aux_file_filt.write('{}\t'.format('o'))

            # SAVE ADVERSARY FILES
            out_file = os.path.join(args.out_path,
            '{fname}.{label}.adversary.{snr}dB.wav'.format(
                fname=fname,
                label=label_list[t],
                snr=int(out_snr+.5)))
            audiolab.wavwrite(x_adv, out_file, fs, fmt)

            out_file2 = os.path.join(args.out_path,
            '{fname}.{label}.adversary.filtered.wav'.format(
                fname=fname,
                label=label_list[t]))
            audiolab.wavwrite(x_filt, out_file2, fs, fmt)

        dnn_file.write('\n'.format(fname))
        dnn_file_filt.write('\n'.format(fname))
        aux_file.write('\n'.format(fname))
        aux_file_filt.write('\n'.format(fname))

    dnn_file.close()
    dnn_file_filt.close()
    aux_file.close()
            print 'Amount: {amount} speakers; Relative Duration: {duration}; Conversation Style: {style}'.format(
                amount=amount, duration=duration, style=freq.keys()[0])

            # Generating conversation
            current_size = 0
            fs = None
            current_data = np.array([])
            change_points = []
            while current_size < duration:
                for speaker_id, speaker_utt_ids in utterances_ids.iteritems():
                    chosen_utt = random.choice(speaker_utt_ids)
                    speaker_samples = audio_dataset[speaker_id].get('speaker_dataset')
                    current_data = np.concatenate((current_data, speaker_samples[chosen_utt].get('raw_data')), axis=0)
                    current_size += speaker_samples[chosen_utt].get('length(secs)')
                    change_points.append(len(current_data))
                    fs = speaker_samples[chosen_utt].get('sample_rate')

            if len(change_points):
                change_points.pop()

            if len(change_points) > 1:
                conversations_dataset = {'id': conversation_id, 'speakers_utterances': utterances_ids,
                                         'speakers_turns': change_points, 'audio_data': current_data,
                                         'length(secs)': current_size, 'sample_rate': fs, 'amount_speakers': amount,
                                         'conversation_style': freq.keys()[0]}
                audlib.wavwrite(np.array(current_data), '{0}.wav'.format(conversation_id), fs)
                conversation_id += 1

                with open('ConversationDataSet.pickle', 'ab') as f:
                    pickle.dump(conversations_dataset, f, -1)
예제 #57
0
파일: runtest.py 프로젝트: ojg/jack-qdsp
def writeaudio(data, filename='test_in.wav'):
    audiolab.wavwrite(data, filename, 48000, 'float32')
예제 #58
0
        X_adv_agg = aggregate_features(dnn_model, X_adv, which_layers)
        p3 = np.argmax(np.bincount(np.array(aux_model.predict(X_adv_agg), dtype='int')))
        print 'Predicted label on adversarial example (classifier trained on aggregated features from last layer of dnn): ', p3


    if args.out_path:        
        out_snr   = 20*np.log10(np.linalg.norm(x[nfft:-nfft]) / np.linalg.norm(x[nfft:-nfft]-x_adv[nfft:-nfft]))
        label_list = ['blues', 'classical', 'country', 'disco', 'hiphop', 'jazz', 'metal', 'pop', 'reggae', 'rock']
        
        out_label1 = label_list[p2]
        out_file1 = os.path.join(args.out_path, 'dnn',
            '{fname}.{label}_adversary.{snr}dB.dnn.wav'.format(fname=os.path.splitext(os.path.split(args.test_file)[-1])[0],
            label=out_label1,
            snr=int(out_snr+.5)))
        audiolab.wavwrite(x_adv, out_file1, fs, 'pcm16')

        if args.aux_model:
            out_label2 = label_list[p3]
            out_file2 = os.path.join(args.out_path, 'rf', 
                '{fname}.{label}_adversary.{snr}dB.rf.wav'.format(fname=os.path.splitext(os.path.split(args.test_file)[-1])[0],
                label=out_label2,
                snr=int(out_snr+.5)))
            audiolab.wavwrite(x_adv, out_file2, fs, 'pcm16')

    if 0:
        ## Time-domain waveforms
        ## ------------------------------------------------------------------------
        plt.ion()
        N = 512
        sup = np.arange(N)