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%pylab inline
This notebook runs through the basic functionality of the current casa.py implementation.
This is calculating the corellogram (subband short-time autocorrelation), estimating pitch-related masks by sampling lags within the corellogram, and masked resynthesis to attempt to separate sources from a mixture.
This package relies on the calc_sbpca being installed in a sibling directory ../calc_sbca/.
It also needs librosa installed.
Dan Ellis dpwe@ee.columbia.edu 2015-04-02
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import numpy as np
import IPython
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sys.path.append('../calc_sbpca/python')
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import librosa
import sbpca
import SAcC
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import casa
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# These are two CRM utterances - one male, one female. They are exactly the same length.
input_wav_file_1 = 'crm-11737.wav'
input_wav_file_2 = 'crm-16515.wav'
d1, sr = SAcC.readwav(input_wav_file_1)
d2, sr = SAcC.readwav(input_wav_file_2)
dmix = d1 + d2
print sr, np.shape(d1), np.shape(d2)
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# This should provide a playback widget
IPython.lib.display.Audio(dmix, rate=sr)
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# To create "oracle" pitch tracks of the two voices in the mixture,
# we'll create & run an SAcC pitch tracker.
sacc_extractor = SAcC.SAcC(SAcC.default_config())
pitches_1 = sacc_extractor(input_wav_file_1)
pitches_2 = sacc_extractor(input_wav_file_2)
# `pitches` consists of time_frames x (time_sec, pitch_hz, voicing_probability).
figure(figsize=(20,4))
plot(pitches_1[:,0], pitches_1[:,1], pitches_2[:,0], pitches_2[:,1])
# The female voice (green line) has a pitch roughly double that of the male.
legend(['crm-11737 (M)', 'crm-16515 (F)'])
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# We can look at the conventional spectrograms of the components and their mix.
n_fft = 512 # 32 ms at SR=16 kHz
hop_length = 160 # 10 ms at SR=16 kHz
D1 = np.abs(librosa.stft(d1, n_fft=n_fft, hop_length=hop_length))
D2 = np.abs(librosa.stft(d2, n_fft=n_fft, hop_length=hop_length))
DMIX = np.abs(librosa.stft(dmix, n_fft=n_fft, hop_length=hop_length))
figure(figsize=(20,6))
maxbin = 200
# Mixture appears in the top pane.
imshow(20.*np.log10(np.vstack([D1[:maxbin, :], D2[:maxbin, :], DMIX[:maxbin, :]])),
origin="bottom", interpolation="nearest", aspect="auto", vmin=-80)
colorbar()
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# Set up the CASA analysis engine.
# Configure filter bank to have narrower bands than the default - for improved separation.
config = casa.test_config
config["fbank_num_bands"] = 72
config["fbank_bpo"] = 12
config["fbank_q"] = 32
print config
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# The CASA "object" provides functions using the filterbank configuration etc.
casobj = casa.casa(config)
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# Create the autocorrelogram of the mixture
acg = casobj.correlogram(dmix)
# Shape is subbands x lags x time frames (10ms step)
print acg.shape
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# The 0th lag is a special case, it shows the overall energy in each t, f cell
# i.e. an auditory-scale spectrogram.
figure(figsize=(20,4))
imshow(10*np.log10(acg[:,0,:]), origin="bottom", interpolation="nearest", aspect="auto")
colorbar()
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# The correlogram is a 3D volume.
# If we slice at a single time, we can see the short-time autocorrelations.
# Frame 40 (t=0.4 s) has a strong mixture of pitches.
frame = 40
# Find the lags corresponding to the two ground-truth pitches at this frame.
# The autocorrelation is forward-looking, so delay the pitch estimates by a couple of 10ms frames.
pitch_delay = 2
expected_lag_1 = np.round(sr/pitches_1[frame + pitch_delay, 1])
expected_lag_2 = np.round(sr/pitches_2[frame + pitch_delay, 1])
figure(figsize=(20,4))
imshow((acg[:, :, frame]), origin="bottom", interpolation="nearest", aspect="auto")
hold(True)
# White vertical stripes indicate the corresponding pitches, which is where we'll sample
# when trying to build the separation masks. They sort-of line up with correlation peaks.
plot([[expected_lag_1, expected_lag_2], [expected_lag_1, expected_lag_2]], [[0,0],[72, 72]], 'w')
axis([0, 400, 0, 72])
colorbar()
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# Now we can construct masks by sampling at the pitches.
# The autocorrelogram is forward looking, so we delay the pitch by a couple of 10ms steps.
num_acg_frames = acg.shape[-1]
env_1 = casobj.env_for_pitch(acg, pitches_1[pitch_delay:pitch_delay + num_acg_frames, 1])
env_2 = casobj.env_for_pitch(acg, pitches_2[pitch_delay:pitch_delay + num_acg_frames, 1])
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# Look at the actual masks.
# The top pane is the mask for the female voice, you can see it picks out a higher fundamental.
figure(figsize=(20,4))
imshow(np.vstack([env_1, env_2]),
origin="bottom", interpolation="nearest", aspect="auto")
colorbar()
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# We can build "hard" masks by seeing which soft mask is bigger in each TF cell
# and we can restrict it to higher-confidence cells by requiring the mask to be larger
# by a factor.
overfactor = 2.0
m_1 = env_1 > overfactor*env_2
m_2 = env_2 > overfactor*env_1
# All the cells not included in either mask.
m_0 = 1 - np.maximum(m_1, m_2)
# `slient_bands` can be used to suppress some channels in resynthesis
# to check the contribution of individual bands.
silent_bands = [] # [x for x in range(72) if x is not 50]
raudio_1 = casobj.apply_tf_mask(d1+d2, m_1, silent_bands)
raudio_2 = casobj.apply_tf_mask(d1+d2, m_2, silent_bands)
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# Listen to the masked resynthesis - Male.
# Note: because this is pitch-based separation, there are no fricatives at all.
IPython.lib.display.Audio(raudio_1, rate=sr)
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# .. and female
IPython.lib.display.Audio(raudio_2, rate=sr)
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# Look at the hard masks.
figure(figsize=(20,4))
imshow(np.vstack([m_1, m_2]),
origin="bottom", interpolation="nearest", aspect="auto")
colorbar()
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# Look at the conventional spectrograms of the resyntheses.
# There's a lot of spread from each subband, so the hard masks end up not being very selective.
DM = np.abs(librosa.stft(d1 + d2, n_fft=n_fft, hop_length=hop_length))
DR1 = np.abs(librosa.stft(raudio_1, n_fft=n_fft, hop_length=hop_length))
DR2 = np.abs(librosa.stft(raudio_2, n_fft=n_fft, hop_length=hop_length))
figure(figsize=(20,8))
imshow(20.*np.log10(np.vstack([DM[:200, :], DR1[:200, :], DR2[:200, :]])),
origin="bottom", interpolation="nearest", aspect="auto", vmin=-80)
colorbar()
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# Look at the actual soft mask values in a single subband.
figure(figsize=(20,4))
band = 20
nframes = env_1.shape[1]
tt = np.arange(nframes)
plot(tt, env_1[band, :], tt, env_2[band, :])
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