def plotstft(audiopath, binsize=2**10, plotpath=None, colormap="jet"):
samplerate, samples = wav.read(audiopath)
frames = wu.get_frames(samples, 1000, 150)
s = stft(frames[200], binsize)
sshow, freq = logscale_spec(s, factor=1.0, sr=samplerate)
ims = 20.*np.log10(np.abs(sshow)/10e-6) # amplitude to decibel
timebins, freqbins = np.shape(ims)
plt.figure(figsize=(15, 7.5))
plt.imshow(np.transpose(ims), origin="lower", aspect="auto", cmap=colormap, interpolation="none")
plt.colorbar()
plt.xlabel("time (s)")
plt.ylabel("frequency (hz)")
plt.xlim([0, timebins-1])
plt.ylim([0, freqbins])
xlocs = np.float32(np.linspace(0, timebins-1, 5))
plt.xticks(xlocs, ["%.02f" % l for l in ((xlocs*len(samples)/timebins)+(0.5*binsize))/samplerate])
ylocs = np.int16(np.round(np.linspace(0, freqbins-1, 10)))
plt.yticks(ylocs, ["%.02f" % freq[i] for i in ylocs])
if plotpath:
plt.savefig(plotpath, bbox_inches="tight")
else:
plt.show()
plt.clf()
#plotstft("my_audio_file.wav")
from features import *
from scipy import signal
import matplotlib.pyplot as plt
from pylab import plot,show,subplot,specgram
#path = 'F:\CETVRTA GODINA\Soft\SoftSound\Test\TestFiles\Woodwinds\BasClarinet\BassClarinet 04.wav'
#path = 'F:\CETVRTA GODINA\Soft\SoftSound\Test\TestFiles\Woodwinds\Flute\Flute 14.wav'
#path = 'F:\CETVRTA GODINA\Soft\SoftSound\Chan Chan (Trimmed).wav'
path = 'F:\CETVRTA GODINA\Diplomski\IRMAS-Sample\IRMAS-Sample\Training\Vio\FormatFactory__[vio][nod][cla]2156__3.wav'
#path = 'F:\CETVRTA GODINA\Soft\SoftSound\PreppedUIowa\Brass\TrumpetNoVib\mono\Trumpet.novib.ff.Eb6.mono.wav'
#spectrogram.plotstft(path)
sample_freq, sound = wavfile.read(path)
frames = wav_utils.get_frames(sound, 10000, 150)
#example = frames[200]
# res = mfcc(sound, sample_freq)
# for i in range(0, 20):
# print res[i]
# print len(res)
#wav_utils.plot_spectrogram(example, sample_freq)
#win = signal.get_window('hamming', 256)
#f, t, Sxx = signal.spectrogram(sound, sample_freq, scaling='spectrum')
#plt.pcolormesh(t, f, Sxx)
#plt.ylabel('Frequency [Hz]')