def hear(self, title=None):
if title is not None:
print("Label:", title)
if self.start is not None or self.end is not None:
print(
f"{round(self.start / self.sr, 2)}s-{round(self.end / self.sr, 2)}s of original clip"
)
start = 0 if self.start is None else self.start
end = len(self.sig) - 1 if self.end is None else self.end
display(
Audio(data=self.data_signal[start:end], rate=self.sample_rate))
else:
display(self.ipy_audio)
def play_transformed(self):
if (self.recon is None):
print "No reconstructed waveform"
return
return Audio(self.recon, rate=self.sr)
def play_original(self):
return Audio(self.orig, rate=self.sr)
def make_audio(self):
"""Makes an IPython Audio object.
"""
audio = Audio(data=self.ys.real, rate=self.frame_rate)
return audio
def ipy_audio(self):
return Audio(data=self.data_signal, rate=self.sample_rate)
def audio_creator():
CHUNK = 1024
FORMAT = pyaudio.paInt16
CHANNELS = 2
RATE = 44100
RECORD_SECONDS = 5
WAVE_OUTPUT_FILENAME = "data1.wav"
BAG_INPUT_FILENAME = 'data1.bag'
WINLEN = float(RATE) / float(CHUNK) #WINLEN=43/second
data_store = []
time_store = []
for topic, msg, t in rosbag.Bag(BAG_INPUT_FILENAME).read_messages():
if msg._type == 'hrl_anomaly_detection/audio':
data_store.append(np.array(msg.audio_data, dtype=np.int16))
time_store.append(t)
# print data_store
# print "\n"
# print time_store
# print "\n"
#copy the frame and insert to lengthen
data_store_long = []
baglen = len(data_store)
num_frames = RATE / CHUNK * RECORD_SECONDS
recovered_len = num_frames / baglen
mfcc_store = []
for frame in data_store:
for i in range(0, recovered_len):
data_store_long.append(frame)
# audio_mfcc = mfcc(frame, samplerate=RATE, nfft=CHUNK, winlen=WINLEN).tolist()[0]
# mfcc_store.append(audio_mfcc)
# print audio_mfcc
# print "\n"
# mfccdata = np.hstack(mfcc_store)
# mfccdata = np.reshape(mfccdata, (len(mfccdata)/CHANNELS, CHANNELS))
# mfccdata = mfcc(numpydata, samplerate=RATE, nfft=CHUNK, winlen=WINLEN).tolist()[0]
# wav.write('data1mfcc_speachfe.wav', RATE, mfccdata)
numpydata = np.hstack(data_store_long)
# mfccdata = mfcc(numpydata, samplerate=RATE, nfft=CHUNK, winlen=WINLEN).tolist()[0]
# wav.write('data1mfcc_speachfe.wav', RATE, mfccdata)
#audio_mfcc = mfcc(numpydata, samplerate=RATE, nfft=CHUNK, winlen=WINLEN).tolist()[0]
#print "audio mfcc"
#print audio_mfcc
#print "\n"
#read WAV - Using numpydata works!(bad quality) but data read from WAV doesn't work.
#Invalid value encountered -- 0. ???
filename = "data1.wav"
y, sr = librosa.load(filename) # notsure why sr = RATE/2
#y = numpydata
#sr = RATE
#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
#^^^^^^^^^Converting to MFCC and reconstructing^^^^^^^^^^^^^^^^^^^^^
#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
#calculate mfccs
Y = librosa.stft(y) #, n_fft=CHUNK, win_length=WINLEN)
mfccs = librosa.feature.mfcc(y) #, sr=sr)
print griffinlim(mfccs)
# print "mfccs"
# print mfccs
#build reconstruction mappings
n_mfcc = mfccs.shape[0]
n_mel = 128
dctm = librosa.filters.dct(n_mfcc, n_mel)
n_fft = 2048
mel_basis = librosa.filters.mel(sr, n_fft)
#Empirical scaling of channels to get ~flat amplitude mapping.
bin_scaling = 1.0 / np.maximum(
0.0005, np.sum(np.dot(mel_basis.T, mel_basis), axis=0))
#Reconstruct the approximate STFT squared-magnitude from the MFCCs.
recon_stft = bin_scaling[:, np.newaxis] * np.dot(
mel_basis.T, invlogamplitude(np.dot(dctm.T, mfccs)))
#Impose reconstructed magnitude on white noise STFT.
excitation = np.random.randn(y.shape[0])
E = librosa.stft(excitation)
recon = librosa.istft(E / np.abs(E) * np.sqrt(recon_stft))
#Listen to the reconstruction.
wav.write('data1mfcc.wav', sr, recon)
Audio(recon, rate=sr)
#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
numpydata = np.reshape(numpydata, (len(numpydata) / CHANNELS, CHANNELS))
outputs.append(last_output)
sys.stdout.write('\n')
def invlogamplitude(S):
"""librosa.logamplitude is actually 10_log10, so invert that."""
return 10.0*(S/10.0)
# Reconstruct audio:
# https://github.com/librosa/librosa/issues/424
mfccs = np.transpose(np.squeeze(np.concatenate(outputs, axis=1), 0))
n_mfcc = mfccs.shape[0]
n_mel = 128
dctm = librosa.filters.dct(n_mfcc, n_mel)
n_fft = 2048
sr = 22050
mel_basis = librosa.filters.mel(sr, n_fft)
bin_scaling = 1.0/np.maximum(0.0005, np.sum(np.dot(mel_basis.T, mel_basis), axis=0))
recon_stft = bin_scaling[:, np.newaxis] * np.dot(mel_basis.T, invlogamplitude(np.dot(dctm.T, mfccs)))
y_len = int(sr * 2.325)
excitation = np.random.randn(y_len)
E = librosa.stft(excitation)
print(np.shape(recon_stft))
print(np.shape(excitation))
print(np.shape(E))
print(recon_stft)
recon = librosa.istft(E/np.abs(E)*np.sqrt(recon_stft))
Audio(recon, rate=sr)