def run(self):
# Initialize signals
self.percentDoneSignal.emit(0)
percent_scale = 1000.0 / 5
self.doneSignal.emit(0)
self.statusSignal.emit("")
# Load in audio data
self.statusSignal.emit("Loading {}".format(
os.path.split(self.mix_file)[1]))
mix, self.fs = librosa.load(self.mix_file, sr=None)
self.percentDoneSignal.emit(1 * percent_scale)
self.statusSignal.emit("Loading {}".format(
os.path.split(self.source_file)[1]))
source, self.fs = librosa.load(self.source_file, sr=self.fs)
self.percentDoneSignal.emit(2 * percent_scale)
# Fix any gross timing offset
self.statusSignal.emit("Aligning...")
mix, source = estimate.align(mix, source, self.fs)
self.percentDoneSignal.emit(3 * percent_scale)
self.statusSignal.emit("Subtracting...")
source = estimate.reverse_channel(mix, source)
mix, source = estimate.pad(mix, source)
self.percentDoneSignal.emit(4 * percent_scale)
self.statusSignal.emit("Enhancing...")
self.subtracted = estimate.wiener_enhance(mix - source, source,
self.wiener_threshold)
self.percentDoneSignal.emit(5 * percent_scale)
self.doneSignal.emit(1)
def run(self):
# Initialize signals
self.percentDoneSignal.emit(0)
percent_scale = 1000.0 / 5
self.doneSignal.emit(0)
self.statusSignal.emit("")
# Load in audio data
self.statusSignal.emit("Loading {}".format(os.path.split(self.mix_file)[1]))
mix, self.fs = librosa.load(self.mix_file, sr=None)
self.percentDoneSignal.emit(1 * percent_scale)
self.statusSignal.emit("Loading {}".format(os.path.split(self.source_file)[1]))
source, self.fs = librosa.load(self.source_file, sr=self.fs)
self.percentDoneSignal.emit(2 * percent_scale)
# Fix any gross timing offset
self.statusSignal.emit("Aligning...")
mix, source = estimate.align(mix, source, self.fs)
self.percentDoneSignal.emit(3 * percent_scale)
self.statusSignal.emit("Subtracting...")
source = estimate.reverse_channel(mix, source)
mix, source = estimate.pad(mix, source)
self.percentDoneSignal.emit(4 * percent_scale)
self.statusSignal.emit("Enhancing...")
self.subtracted = estimate.wiener_enhance(mix - source, source, self.wiener_threshold)
self.percentDoneSignal.emit(5 * percent_scale)
self.doneSignal.emit(1)
for subdirectory in [d for d in subdirectories if os.path.isdir(d)]:
print 'Processing file {}'.format(subdirectory)
# Load in the full mixture
mix, fs = librosa.load(os.path.join(subdirectory, 'M.wav'), sr=None)
# Perform timing/channel estimation for both a cappella ('A' files) and instrumental ('I' files)
for s in ['A', 'I']:
# Load in the source waveform
source, fs = librosa.load(os.path.join(subdirectory,
'{}.wav'.format(s)),
sr=fs)
# Align the source to the mixture
# Small resampling rate range because it's all digital
mix, source_aligned = estimate.align(mix,
source,
fs,
correlation_size=4.,
max_global_offset=0.,
max_skew=.0001,
hop=1.,
max_local_offset=.01)
# Estimate the filter
source_filtered = estimate.reverse_channel(mix, source_aligned)
# Write out the aligned and filtered versions
librosa.output.write_wav(
os.path.join(subdirectory, '{}-aligned.wav'.format(s)),
source_aligned, fs)
librosa.output.write_wav(
os.path.join(subdirectory, '{}-aligned-filtered.wav'.format(s)),
source_filtered, fs)
# <markdowncell>
# Path to .wav files ripped from CDs...
data_directory = '../Dataset/Digital/'
# ... with subdirectories 1, 2, 3... each for a different song
subdirectories = [os.path.join(data_directory, d) for d in os.listdir(data_directory)]
for subdirectory in [d for d in subdirectories if os.path.isdir(d)]:
print 'Processing file {}'.format(subdirectory)
# Load in the full mixture
mix, fs = librosa.load(os.path.join(subdirectory, 'M.wav'), sr=None)
# Perform timing/channel estimation for both a cappella ('A' files) and instrumental ('I' files)
for s in ['A', 'I']:
# Load in the source waveform
source, fs = librosa.load(os.path.join(subdirectory, '{}.wav'.format(s)), sr=fs)
# Align the source to the mixture
# Small resampling rate range because it's all digital
mix, source_aligned = estimate.align(mix, source, fs, correlation_size=4., max_global_offset=0.,
max_skew=.0001, hop=1., max_local_offset=.01)
# Estimate the filter
source_filtered = estimate.reverse_channel(mix, source_aligned)
# Write out the aligned and filtered versions
librosa.output.write_wav(os.path.join(subdirectory, '{}-aligned.wav'.format(s)), source_aligned, fs)
librosa.output.write_wav(os.path.join(subdirectory, '{}-aligned-filtered.wav'.format(s)), source_filtered, fs)
# <markdowncell>
# ## Step 2: Compute SDR
# For each song, we compute the SDR of $m - c$, $m - c_\mathcal{R}$ and $m - c_\mathcal{F}$ relative to $s$ where we consider both the case when $c$ is the a cappella and $s$ is the instrumental and the case where $c$ is the instrumental and $s$ is the a cappella.
# <codecell>
def trim(signals, trim_length):
'''