def main(inwavfile, outmp3file, bitrate):
"""ENCODER MAIN FUNCTION"""
# Read WAVE file and set MPEG encoder parameters
input_buffer = WavRead(inwavfile)
params = EncoderParameters(input_buffer.fs, input_buffer.nch, bitrate)
# Read baseband filter samples
baseband_filter = prototype_filter().astype = ('float32')
subband_samples = np.zeros((params.nch, N_SUBBANDS, FRAMES_PER_BLOCK),
dtype='float32')
# Main loop executing until all samples have been processed
while input_buffer.nprocessed_samples < input_buffer.nsamples:
# In each block 12 frames are processed, which equals 12x32=384 samples per block
for frm in range(FRAMES_PER_BLOCK):
samples_read = input_buffer.read_sample(SHIFT_SIZE)
# Perform zero padding if all samples have been read
if samples_read < SHIFT_SIZE:
input_buffer.audio[ch].insert(
np.zeros(SHIFT_SIZE - samples_read))
# Filtering = dot product with reverse buffer
for ch in range(params.nch):
subband_samples[ch, :, frm] = subband_filtering(
input_buffer.audio[ch].reversed(), baseband_filter)
# Declaring arrays for keeping table indices of calculated scalefactors and bits allocated in subbands.
# Number of bits allocated in subband is either 0 or in range [2,15]
sfcindices = np.zeros((params.nch, N_SUBBANDS), dtype='uint8')
subband_bit_allocation = np.zeros((params.nch, N_SUBBANDS),
dtype='uint8')
smr = np.zeros((params.nch, N_SUBBANDS), dtype='float32')
# Finding scale factors, psychoacoustic model and bit allocation calculation for subbands. Although scaling is done later,
# its result is necessary for the psychoacoustic model and calculation of sound pressure levels.
for ch in range(params.nch):
sfcindices[ch, :] = get_scalefactors(subband_samples[ch, :, :],
params.table.scalefactor)
subband_bit_allocation[ch, :] = psycho.model1(
input_buffer.audio[ch].ordered(), params, sfindices)
subband_samples_quantized = np.zeros(subband_samples.shape,
dtype='uint32')
for ch in range(params.nch):
for sb in range(N_SUBBANDS):
QCa = params.table.qca[subband_bit_allocation[ch, sb] - 2]
QCb = params.table.qcb[subband_bit_allocation[ch, sb] - 2]
scf = params.table.scalefactor[sfcindices[ch, sb]]
ba = subband_bit_allocation[ch, sb]
for ind in (FRAMES_PER_BLOCK):
subband_samples_quantized[ch, sb, ind] = quantization(
subband_samples[ch, sb, ind], scf, ba, QCa, QCb)
# Fromatting output bitstream and appending it to the output file
bitstream_formatting(outmp3file, params, subband_bit_allocation,
sfcindices, subband_samples_quantized)
def encode(input_buffer,params,outmp3file,**kwargs):
"""Encode the rest of the file. If uniform=true, another file with uniform quantization is created."""
uniform = kwargs.get('uniform', False)
if uniform:
params_uniform = EncoderParameters(input_buffer.fs, input_buffer.nch, params.bitrate)
uniform_bit_allocation = np.zeros((params.nch, N_SUBBANDS), dtype='uint8')
for ch in range(params.nch):
uniform_bit_allocation[ch,:] = psychoacoustic.smr_bit_allocation(params, np.zeros(N_SUBBANDS))
# Read baseband filter samples
baseband_filter = filter_coeffs()
# Allocate space for 32 subband filters of length 512.
filterbank = np.zeros((N_SUBBANDS, FRAME_SIZE), dtype='float32')
# Perform modulation.
for sb in range(N_SUBBANDS):
for n in range(FRAME_SIZE):
filterbank[sb,n] = baseband_filter[n] * np.cos((2 * sb + 1) * (n - 16 ) * np.pi / 64)
subband_samples = np.zeros((params.nch, N_SUBBANDS, FRAMES_PER_BLOCK), dtype='float32')
# Main loop, executing until all samples have been processed.
while input_buffer.nprocessed_samples < input_buffer.nsamples:
# In each block 12 frames are processed, which equals 12x32=384 new samples per block.
for frm in range(FRAMES_PER_BLOCK):
samples_read = input_buffer.read_samples(SHIFT_SIZE)
# If all samples have been read, perform zero padding.
if samples_read < SHIFT_SIZE:
for ch in range(params.nch):
input_buffer.audio[ch].insert(np.zeros(SHIFT_SIZE - samples_read))
# Filtering = dot product with reversed buffer.
for ch in range(params.nch):
subband_samples[ch,:,frm] = np.dot(filterbank, input_buffer.audio[ch].reversed())
# Declaring arrays for keeping table indices of calculated scalefactors and bits allocated in subbands.
scfindices = np.zeros((params.nch, N_SUBBANDS), dtype='uint8')
subband_bit_allocation = np.zeros((params.nch, N_SUBBANDS), dtype='uint8')
# Finding scale factors, psychoacoustic model and bit allocation calculation for subbands. Although
# scaling is done later, its result is necessary for the psychoacoustic model and calculation of
# sound pressure levels.
for ch in range(params.nch):
scfindices[ch,:] = get_scalefactors(subband_samples[ch,:,:], params.table.scalefactor)
subband_bit_allocation[ch,:] = psychoacoustic.model1(input_buffer.audio[ch].ordered(), params, scfindices)
# Scaling subband samples with determined scalefactors.
for ind in range(FRAMES_PER_BLOCK):
subband_samples[:,:,ind] /= params.table.scalefactor[scfindices]
if uniform:
subband_samples_uniform = np.copy(subband_samples)
# Subband samples quantization. Multiplication with coefficients 'a' and adding coefficients 'b' is
# defined in the ISO standard.
subband_samples_quantized = subband_samples
for ch in range(params.nch):
for sb in range(N_SUBBANDS):
if subband_bit_allocation[ch,sb] != 0:
subband_samples[ch,sb,:] *= params.table.qca[subband_bit_allocation[ch,sb] - 2]
subband_samples[ch,sb,:] += params.table.qcb[subband_bit_allocation[ch,sb] - 2]
subband_samples[ch,sb,:] *= 1<<subband_bit_allocation[ch,sb] - 1
# Since subband_samples is a float array, it needs to be cast to unsigned integers.
subband_samples_quantized = subband_samples.astype('uint32')
# Forming output bitsream and appending it to the output file.
bitstream_formatting(outmp3file,
params,
subband_bit_allocation,
scfindices,
subband_samples_quantized)
if uniform:
for ch in range(params.nch):
for sb in range(N_SUBBANDS):
if uniform_bit_allocation[ch,sb] != 0:
subband_samples_uniform[ch,sb,:] *= params_uniform.table.qca[uniform_bit_allocation[ch,sb] - 2]
subband_samples_uniform[ch,sb,:] += params_uniform.table.qcb[uniform_bit_allocation[ch,sb] - 2]
subband_samples_uniform[ch,sb,:] *= 1<<uniform_bit_allocation[ch,sb] - 1
subband_samples_uniform = subband_samples_uniform.astype('uint32')
bitstream_formatting(outmp3file[:-4] + '_uniform' + outmp3file[-4:],
params_uniform,
uniform_bit_allocation,
scfindices,
subband_samples_uniform)
def main(inwavfile, outmp3file, bitrate):
"""Encoder main function."""
#inwavfile = "../samples/sinestereo.wav"
#outmp3file = "../samples/sinestereo.mp3"
#bitrate = 320
# Read WAVE file and set MPEG encoder parameters.
input_buffer = WavRead(inwavfile)
params = EncoderParameters(input_buffer.fs, input_buffer.nch, bitrate)
# Subband filter calculation from baseband prototype.
# Very detailed analysis of MP3 subband filtering available at
# http://cnx.org/content/m32148/latest/?collection=col11121/latest
# Read baseband filter samples
"""
Prototype-filter
"""
baseband_filter = prototype_filter.prototype_filter().astype('float32')
subband_samples = np.zeros((params.nch, N_SUBBANDS, FRAMES_PER_BLOCK),
dtype='float32')
# Main loop, executing until all samples have been processed.
while input_buffer.nprocessed_samples < input_buffer.nsamples:
# In each block 12 frames are processed, which equals 12x32=384 new samples per block.
for frm in range(FRAMES_PER_BLOCK):
samples_read = input_buffer.read_samples(SHIFT_SIZE)
# If all samples have been read, perform zero padding.
if samples_read < SHIFT_SIZE:
for ch in range(params.nch):
input_buffer.audio[ch].insert(
np.zeros(SHIFT_SIZE - samples_read))
# Filtering = dot product with reversed buffer.
"""
Subband filtering
"""
for ch in range(params.nch):
subband_samples[ch, :,
frm] = subband_filtering.subband_filtering(
input_buffer.audio[ch].reversed(),
baseband_filter)
# Declaring arrays for keeping table indices of calculated scalefactors and bits allocated in subbands.
# Number of bits allocated in subband is either 0 or in range [2,15].
scfindices = np.zeros((params.nch, N_SUBBANDS), dtype='uint8')
subband_bit_allocation = np.zeros((params.nch, N_SUBBANDS),
dtype='uint8')
smr = np.zeros((params.nch, N_SUBBANDS), dtype='float32')
# Finding scale factors, psychoacoustic model and bit allocation calculation for subbands. Although
# scaling is done later, its result is necessary for the psychoacoustic model and calculation of
# sound pressure levels.
for ch in range(params.nch):
scfindices[ch, :] = get_scalefactors(subband_samples[ch, :, :],
params.table.scalefactor)
subband_bit_allocation[ch, :] = psycho.model1(
input_buffer.audio[ch].ordered(), params, scfindices)
"""
Quantization
"""
subband_samples_quantized = np.zeros(subband_samples.shape,
dtype='uint32')
for ch in range(params.nch):
for sb in range(N_SUBBANDS):
QCa = params.table.qca[subband_bit_allocation[ch, sb] - 2]
QCb = params.table.qcb[subband_bit_allocation[ch, sb] - 2]
scf = params.table.scalefactor[scfindices[ch, sb]]
ba = subband_bit_allocation[ch, sb]
for ind in range(FRAMES_PER_BLOCK):
subband_samples_quantized[ch, sb,
ind] = quantization.quantization(
subband_samples[ch, sb, ind],
scf, ba, QCa, QCb)
# Forming output bitsream and appending it to the output file.
bitstream_formatting(outmp3file, params, subband_bit_allocation,
scfindices, subband_samples_quantized)
def main(inwavfile, outmp3file, bitrate):
"""Encoder main function."""
#inwavfile = "../samples/sinestereo.wav"
#outmp3file = "../samples/sinestereo.mp3"
#bitrate = 320
# Read WAVE file and set MPEG encoder parameters.
input_buffer = WavRead(inwavfile)
params = EncoderParameters(input_buffer.fs, input_buffer.nch, bitrate)
# Subband filter calculation from baseband prototype.
# Very detailed analysis of MP3 subband filtering available at
# http://cnx.org/content/m32148/latest/?collection=col11121/latest
# Read baseband filter samples
baseband_filter = filter_coeffs()
# Allocate space for 32 subband filters of length 512.
filterbank = np.zeros((N_SUBBANDS, FRAME_SIZE), dtype='float32')
# Perform modulation.
for sb in range(N_SUBBANDS):
for n in range(FRAME_SIZE):
filterbank[sb, n] = baseband_filter[n] * np.cos(
(2 * sb + 1) * (n - 16) * np.pi / 64)
subband_samples = np.zeros((params.nch, N_SUBBANDS, FRAMES_PER_BLOCK),
dtype='float32')
# Main loop, executing until all samples have been processed.
while input_buffer.nprocessed_samples < input_buffer.nsamples:
# In each block 12 frames are processed, which equals 12x32=384 new samples per block.
for frm in range(FRAMES_PER_BLOCK):
samples_read = input_buffer.read_samples(SHIFT_SIZE)
# If all samples have been read, perform zero padding.
if samples_read < SHIFT_SIZE:
for ch in range(params.nch):
input_buffer.audio[ch].insert(
np.zeros(SHIFT_SIZE - samples_read))
# Filtering = dot product with reversed buffer.
for ch in range(params.nch):
subband_samples[ch, :, frm] = np.dot(
filterbank, input_buffer.audio[ch].reversed())
# Declaring arrays for keeping table indices of calculated scalefactors and bits allocated in subbands.
# Number of bits allocated in subband is either 0 or in range [2,15].
scfindices = np.zeros((params.nch, N_SUBBANDS), dtype='uint8')
subband_bit_allocation = np.zeros((params.nch, N_SUBBANDS),
dtype='uint8')
smr = np.zeros((params.nch, N_SUBBANDS), dtype='float32')
# Finding scale factors, psychoacoustic model and bit allocation calculation for subbands. Although
# scaling is done later, its result is necessary for the psychoacoustic model and calculation of
# sound pressure levels.
for ch in range(params.nch):
scfindices[ch, :] = get_scalefactors(subband_samples[ch, :, :],
params.table.scalefactor)
subband_bit_allocation[ch, :] = psycho.model1(
input_buffer.audio[ch].ordered(), params, scfindices)
# Scaling subband samples with determined scalefactors.
for ind in range(FRAMES_PER_BLOCK):
subband_samples[:, :, ind] /= params.table.scalefactor[scfindices]
# Subband samples quantization. Multiplication with coefficients 'a' and adding coefficients 'b' is
# defined in the ISO standard.
for ch in range(params.nch):
for sb in range(N_SUBBANDS):
if subband_bit_allocation[ch, sb] != 0:
subband_samples[ch, sb, :] *= params.table.qca[
subband_bit_allocation[ch, sb] - 2]
subband_samples[ch, sb, :] += params.table.qcb[
subband_bit_allocation[ch, sb] - 2]
subband_samples[
ch, sb, :] *= 1 << subband_bit_allocation[ch, sb] - 1
# Since subband_samples is a float array, it needs to be cast to unsigned integers.
subband_samples_quantized = subband_samples.astype('uint32')
# Forming output bitsream and appending it to the output file.
bitstream_formatting(outmp3file, params, subband_bit_allocation,
scfindices, subband_samples_quantized)
def main(inwavfile, outmp3file, bitrate):
"""Encoder main function."""
#inwavfile = "../samples/sinestereo.wav"
#outmp3file = "../samples/sinestereo.mp3"
#bitrate = 320
# Read WAVE file and set MPEG encoder parameters.
input_buffer = WavRead(inwavfile)
params = EncoderParameters(input_buffer.fs, input_buffer.nch, bitrate)
# Subband filter calculation from baseband prototype.
# Very detailed analysis of MP3 subband filtering available at
# http://cnx.org/content/m32148/latest/?collection=col11121/latest
# Read baseband filter samples
"""
ASSIGNMENT 2
"""
baseband_filter = assignment2.prototype_filter().astype('float32')
subband_samples = np.zeros((params.nch, N_SUBBANDS, FRAMES_PER_BLOCK), dtype='float32')
# Main loop, executing until all samples have been processed.
while input_buffer.nprocessed_samples < input_buffer.nsamples:
# In each block 12 frames are processed, which equals 12x32=384 new samples per block.
for frm in range(FRAMES_PER_BLOCK):
samples_read = input_buffer.read_samples(SHIFT_SIZE)
# If all samples have been read, perform zero padding.
if samples_read < SHIFT_SIZE:
for ch in range(params.nch):
input_buffer.audio[ch].insert(np.zeros(SHIFT_SIZE - samples_read))
# Filtering = dot product with reversed buffer.
"""
ASSIGNMENT 3 : Subband filtering
"""
for ch in range(params.nch):
subband_samples[ch,:,frm] = assignment3.subband_filtering(input_buffer.audio[ch].reversed(), baseband_filter)
# Declaring arrays for keeping table indices of calculated scalefactors and bits allocated in subbands.
# Number of bits allocated in subband is either 0 or in range [2,15].
scfindices = np.zeros((params.nch, N_SUBBANDS), dtype='uint8')
subband_bit_allocation = np.zeros((params.nch, N_SUBBANDS), dtype='uint8')
smr = np.zeros((params.nch, N_SUBBANDS), dtype='float32')
# Finding scale factors, psychoacoustic model and bit allocation calculation for subbands. Although
# scaling is done later, its result is necessary for the psychoacoustic model and calculation of
# sound pressure levels.
for ch in range(params.nch):
scfindices[ch,:] = get_scalefactors(subband_samples[ch,:,:], params.table.scalefactor)
subband_bit_allocation[ch,:] = psycho.model1(input_buffer.audio[ch].ordered(), params,scfindices)
"""
ASSIGNMENT 4 : Quantization
"""
subband_samples_quantized = np.zeros(subband_samples.shape, dtype='uint32')
for ch in range(params.nch):
for sb in range(N_SUBBANDS):
QCa = params.table.qca[subband_bit_allocation[ch,sb]-2]
QCb = params.table.qcb[subband_bit_allocation[ch,sb]-2]
scf = params.table.scalefactor[scfindices[ch,sb]]
ba = subband_bit_allocation[ch,sb]
for ind in range(FRAMES_PER_BLOCK):
subband_samples_quantized[ch,sb,ind] = assignment4.quantization(subband_samples[ch,sb,ind], scf, ba, QCa, QCb)
# Forming output bitsream and appending it to the output file.
bitstream_formatting(outmp3file,
params,
subband_bit_allocation,
scfindices,
subband_samples_quantized)
def main(inwavfile, outmp3file, bitrate):
"""Encoder main function."""
#inwavfile = "../samples/sinestereo.wav"
#outmp3file = "../samples/sinestereo.mp3"
#bitrate = 320
# Read WAVE file and set MPEG encoder parameters.
input_buffer = WavRead(inwavfile)
params = EncoderParameters(input_buffer.fs, input_buffer.nch, bitrate)
# Subband filter calculation from baseband prototype.
# Very detailed analysis of MP3 subband filtering available at
# http://cnx.org/content/m32148/latest/?collection=col11121/latest
# Read baseband filter samples
baseband_filter = filter_coeffs()
# Allocate space for 32 subband filters of length 512.
filterbank = np.zeros((N_SUBBANDS, FRAME_SIZE), dtype='float32')
# Perform modulation.
for sb in range(N_SUBBANDS):
for n in range(FRAME_SIZE):
filterbank[sb,n] = baseband_filter[n] * np.cos((2 * sb + 1) * (n - 16) * np.pi / 64)
subband_samples = np.zeros((params.nch, N_SUBBANDS, FRAMES_PER_BLOCK), dtype='float32')
# Main loop, executing until all samples have been processed.
while input_buffer.nprocessed_samples < input_buffer.nsamples:
# In each block 12 frames are processed, which equals 12x32=384 new samples per block.
for frm in range(FRAMES_PER_BLOCK):
samples_read = input_buffer.read_samples(SHIFT_SIZE)
# If all samples have been read, perform zero padding.
if samples_read < SHIFT_SIZE:
for ch in range(params.nch):
input_buffer.audio[ch].insert(np.zeros(SHIFT_SIZE - samples_read))
# Filtering = dot product with reversed buffer.
for ch in range(params.nch):
subband_samples[ch,:,frm] = np.dot(filterbank, input_buffer.audio[ch].reversed())
# Declaring arrays for keeping table indices of calculated scalefactors and bits allocated in subbands.
# Number of bits allocated in subband is either 0 or in range [2,15].
scfindices = np.zeros((params.nch, N_SUBBANDS), dtype='uint8')
subband_bit_allocation = np.zeros((params.nch, N_SUBBANDS), dtype='uint8')
smr = np.zeros((params.nch, N_SUBBANDS), dtype='float32')
# Finding scale factors, psychoacoustic model and bit allocation calculation for subbands. Although
# scaling is done later, its result is necessary for the psychoacoustic model and calculation of
# sound pressure levels.
for ch in range(params.nch):
scfindices[ch,:] = get_scalefactors(subband_samples[ch,:,:], params.table.scalefactor)
subband_bit_allocation[ch,:] = psycho.model1(input_buffer.audio[ch].ordered(), params,scfindices)
# Scaling subband samples with determined scalefactors.
for ind in range(FRAMES_PER_BLOCK):
subband_samples[:,:,ind] /= params.table.scalefactor[scfindices]
# Subband samples quantization. Multiplication with coefficients 'a' and adding coefficients 'b' is
# defined in the ISO standard.
for ch in range(params.nch):
for sb in range(N_SUBBANDS):
if subband_bit_allocation[ch,sb] != 0:
subband_samples[ch,sb,:] *= params.table.qca[subband_bit_allocation[ch,sb] - 2]
subband_samples[ch,sb,:] += params.table.qcb[subband_bit_allocation[ch,sb] - 2]
subband_samples[ch,sb,:] *= 1<<subband_bit_allocation[ch,sb] - 1
# Since subband_samples is a float array, it needs to be cast to unsigned integers.
subband_samples_quantized = subband_samples.astype('uint32')
# Forming output bitsream and appending it to the output file.
bitstream_formatting(outmp3file,
params,
subband_bit_allocation,
scfindices,
subband_samples_quantized)
def encode(input_buffer, params, outmp3file, **kwargs):
"""Encode the rest of the file. If uniform=true, another file with uniform quantization is created."""
uniform = kwargs.get('uniform', False)
if uniform:
params_uniform = EncoderParameters(input_buffer.fs, input_buffer.nch,
params.bitrate)
uniform_bit_allocation = np.zeros((params.nch, N_SUBBANDS),
dtype='uint8')
for ch in range(params.nch):
uniform_bit_allocation[ch, :] = psychoacoustic.smr_bit_allocation(
params, np.zeros(N_SUBBANDS))
# Read baseband filter samples
baseband_filter = filter_coeffs()
# Allocate space for 32 subband filters of length 512.
filterbank = np.zeros((N_SUBBANDS, FRAME_SIZE), dtype='float32')
# Perform modulation.
for sb in range(N_SUBBANDS):
for n in range(FRAME_SIZE):
filterbank[sb, n] = baseband_filter[n] * np.cos(
(2 * sb + 1) * (n - 16) * np.pi / 64)
subband_samples = np.zeros((params.nch, N_SUBBANDS, FRAMES_PER_BLOCK),
dtype='float32')
# Main loop, executing until all samples have been processed.
while input_buffer.nprocessed_samples < input_buffer.nsamples:
# In each block 12 frames are processed, which equals 12x32=384 new samples per block.
for frm in range(FRAMES_PER_BLOCK):
samples_read = input_buffer.read_samples(SHIFT_SIZE)
# If all samples have been read, perform zero padding.
if samples_read < SHIFT_SIZE:
for ch in range(params.nch):
input_buffer.audio[ch].insert(
np.zeros(SHIFT_SIZE - samples_read))
# Filtering = dot product with reversed buffer.
for ch in range(params.nch):
subband_samples[ch, :, frm] = np.dot(
filterbank, input_buffer.audio[ch].reversed())
# Declaring arrays for keeping table indices of calculated scalefactors and bits allocated in subbands.
scfindices = np.zeros((params.nch, N_SUBBANDS), dtype='uint8')
subband_bit_allocation = np.zeros((params.nch, N_SUBBANDS),
dtype='uint8')
# Finding scale factors, psychoacoustic model and bit allocation calculation for subbands. Although
# scaling is done later, its result is necessary for the psychoacoustic model and calculation of
# sound pressure levels.
for ch in range(params.nch):
scfindices[ch, :] = get_scalefactors(subband_samples[ch, :, :],
params.table.scalefactor)
subband_bit_allocation[ch, :] = psychoacoustic.model1(
input_buffer.audio[ch].ordered(), params, scfindices)
# Scaling subband samples with determined scalefactors.
for ind in range(FRAMES_PER_BLOCK):
subband_samples[:, :, ind] /= params.table.scalefactor[scfindices]
if uniform:
subband_samples_uniform = np.copy(subband_samples)
# Subband samples quantization. Multiplication with coefficients 'a' and adding coefficients 'b' is
# defined in the ISO standard.
subband_samples_quantized = subband_samples
for ch in range(params.nch):
for sb in range(N_SUBBANDS):
if subband_bit_allocation[ch, sb] != 0:
subband_samples[ch, sb, :] *= params.table.qca[
subband_bit_allocation[ch, sb] - 2]
subband_samples[ch, sb, :] += params.table.qcb[
subband_bit_allocation[ch, sb] - 2]
subband_samples[
ch, sb, :] *= 1 << subband_bit_allocation[ch, sb] - 1
# Since subband_samples is a float array, it needs to be cast to unsigned integers.
subband_samples_quantized = subband_samples.astype('uint32')
# Forming output bitsream and appending it to the output file.
bitstream_formatting(outmp3file, params, subband_bit_allocation,
scfindices, subband_samples_quantized)
if uniform:
for ch in range(params.nch):
for sb in range(N_SUBBANDS):
if uniform_bit_allocation[ch, sb] != 0:
subband_samples_uniform[
ch, sb, :] *= params_uniform.table.qca[
uniform_bit_allocation[ch, sb] - 2]
subband_samples_uniform[
ch, sb, :] += params_uniform.table.qcb[
uniform_bit_allocation[ch, sb] - 2]
subband_samples_uniform[
ch,
sb, :] *= 1 << uniform_bit_allocation[ch, sb] - 1
subband_samples_uniform = subband_samples_uniform.astype('uint32')
bitstream_formatting(
outmp3file[:-4] + '_uniform' + outmp3file[-4:], params_uniform,
uniform_bit_allocation, scfindices, subband_samples_uniform)