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Merge pull request #414 from librosa/window-bandwidth
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All the windows in CQT
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@bmcfee
bmcfee committed Oct 4, 2016
2 parents e5d78d4 + 04fed1d commit b0e7669
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Showing 3 changed files with 120 additions and 54 deletions.
73 changes: 49 additions & 24 deletions librosa/core/constantq.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,8 +3,6 @@
'''Pitch-tracking and tuning estimation'''
from __future__ import division

from warnings import warn

import numpy as np
import scipy.fftpack as fft

Expand All @@ -23,7 +21,8 @@
@cache(level=20)
def cqt(y, sr=22050, hop_length=512, fmin=None, n_bins=84,
bins_per_octave=12, tuning=None, filter_scale=1,
norm=1, sparsity=0.01, real=util.Deprecated()):
norm=1, sparsity=0.01, window='hann',
real=util.Deprecated()):
'''Compute the constant-Q transform of an audio signal.
This implementation is based on the recursive sub-sampling method
Expand Down Expand Up @@ -72,6 +71,10 @@ def cqt(y, sr=22050, hop_length=512, fmin=None, n_bins=84,
Set `sparsity=0` to disable sparsification.
window : str, tuple, number, or function
Window specification for the basis filters.
See `filters.get_window` for details.
real : [DEPRECATED]
.. warning:: This parameter name deprecated in librosa 0.5.0
It will be removed in librosa 0.6.0.
Expand Down Expand Up @@ -159,7 +162,7 @@ def cqt(y, sr=22050, hop_length=512, fmin=None, n_bins=84,

# Determine required resampling quality
Q = float(filter_scale) / (2.0**(1. / bins_per_octave) - 1)
filter_cutoff = fmax_t * (1 + filters.window_bandwidth('hann') / Q)
filter_cutoff = fmax_t * (1 + 0.5 * filters.window_bandwidth(window) / Q)
nyquist = sr / 2.0
if filter_cutoff < audio.BW_FASTEST * nyquist:
res_type = 'kaiser_fast'
Expand All @@ -182,7 +185,8 @@ def cqt(y, sr=22050, hop_length=512, fmin=None, n_bins=84,
tuning,
filter_scale,
norm,
sparsity)
sparsity,
window=window)

# Compute the CQT filter response and append it to the stack
cqt_resp.append(__cqt_response(y, n_fft, hop_length, fft_basis))
Expand All @@ -191,7 +195,7 @@ def cqt(y, sr=22050, hop_length=512, fmin=None, n_bins=84,
fmax_t /= 2
n_octaves -= 1

filter_cutoff = fmax_t * (1 + filters.window_bandwidth('hann') / Q)
filter_cutoff = fmax_t * (1 + 0.5 * filters.window_bandwidth(window) / Q)

res_type = 'kaiser_fast'

Expand All @@ -209,7 +213,8 @@ def cqt(y, sr=22050, hop_length=512, fmin=None, n_bins=84,
tuning,
filter_scale,
norm,
sparsity)
sparsity,
window=window)

my_y, my_sr, my_hop = y, sr, hop_length

Expand All @@ -220,9 +225,11 @@ def cqt(y, sr=22050, hop_length=512, fmin=None, n_bins=84,
if i > 0:
if len(my_y) < 2:
raise ParameterError('Input signal length={} is too short for '
'{:d}-octave CQT'.format(len_orig, n_octaves))
'{:d}-octave CQT'.format(len_orig,
n_octaves))

# The additional scaling of sqrt(2) here is to implicitly rescale the filters
# The additional scaling of sqrt(2) here is to implicitly rescale
# the filters
my_y = np.sqrt(2) * audio.resample(my_y, my_sr, my_sr/2.0,
res_type=res_type,
scale=True)
Expand All @@ -232,14 +239,13 @@ def cqt(y, sr=22050, hop_length=512, fmin=None, n_bins=84,
# Compute the cqt filter response and append to the stack
cqt_resp.append(__cqt_response(my_y, n_fft, my_hop, fft_basis))


return __trim_stack(cqt_resp, n_bins)


@cache(level=20)
def hybrid_cqt(y, sr=22050, hop_length=512, fmin=None, n_bins=84,
bins_per_octave=12, tuning=None, filter_scale=1,
norm=1, sparsity=0.01):
norm=1, sparsity=0.01, window='hann'):
'''Compute the hybrid constant-Q transform of an audio signal.
Here, the hybrid CQT uses the pseudo CQT for higher frequencies where
Expand Down Expand Up @@ -280,6 +286,11 @@ def hybrid_cqt(y, sr=22050, hop_length=512, fmin=None, n_bins=84,
Set `sparsity=0` to disable sparsification.
window : str, tuple, number, or function
Window specification for the basis filters.
See `filters.get_window` for details.
Returns
-------
CQT : np.ndarray [shape=(n_bins, t), dtype=np.float]
Expand Down Expand Up @@ -321,7 +332,8 @@ def hybrid_cqt(y, sr=22050, hop_length=512, fmin=None, n_bins=84,
n_bins=n_bins,
bins_per_octave=bins_per_octave,
tuning=tuning,
filter_scale=filter_scale)
filter_scale=filter_scale,
window=window)

# Determine which filters to use with Pseudo CQT
# These are the ones that fit within 2 hop lengths after padding
Expand All @@ -342,7 +354,8 @@ def hybrid_cqt(y, sr=22050, hop_length=512, fmin=None, n_bins=84,
tuning=tuning,
filter_scale=filter_scale,
norm=norm,
sparsity=sparsity))
sparsity=sparsity,
window=window))

if n_bins_full > 0:
cqt_resp.append(np.abs(cqt(y, sr,
Expand All @@ -353,15 +366,16 @@ def hybrid_cqt(y, sr=22050, hop_length=512, fmin=None, n_bins=84,
tuning=tuning,
filter_scale=filter_scale,
norm=norm,
sparsity=sparsity)))
sparsity=sparsity,
window=window)))

return __trim_stack(cqt_resp, n_bins)


@cache(level=20)
def pseudo_cqt(y, sr=22050, hop_length=512, fmin=None, n_bins=84,
bins_per_octave=12, tuning=None, filter_scale=1,
norm=1, sparsity=0.01):
norm=1, sparsity=0.01, window='hann'):
'''Compute the pseudo constant-Q transform of an audio signal.
This uses a single fft size that is the smallest power of 2 that is greater
Expand Down Expand Up @@ -404,6 +418,10 @@ def pseudo_cqt(y, sr=22050, hop_length=512, fmin=None, n_bins=84,
Set `sparsity=0` to disable sparsification.
window : str, tuple, number, or function
Window specification for the basis filters.
See `filters.get_window` for details.
Returns
-------
Expand All @@ -417,7 +435,7 @@ def pseudo_cqt(y, sr=22050, hop_length=512, fmin=None, n_bins=84,
`2**(n_bins / bins_per_octave)`
Or if `y` is too short to support the frequency range of the CQT.
Notes
-----
This function caches at level 20.
Expand All @@ -435,7 +453,8 @@ def pseudo_cqt(y, sr=22050, hop_length=512, fmin=None, n_bins=84,
bins_per_octave,
tuning, filter_scale,
norm, sparsity,
hop_length=hop_length)
hop_length=hop_length,
window=window)

fft_basis = np.abs(fft_basis)

Expand All @@ -448,7 +467,8 @@ def pseudo_cqt(y, sr=22050, hop_length=512, fmin=None, n_bins=84,

@cache(level=10)
def __cqt_filter_fft(sr, fmin, n_bins, bins_per_octave, tuning,
filter_scale, norm, sparsity, hop_length=None):
filter_scale, norm, sparsity, hop_length=None,
window='hann'):
'''Generate the frequency domain constant-Q filter basis.'''

basis, lengths = filters.constant_q(sr,
Expand All @@ -458,12 +478,15 @@ def __cqt_filter_fft(sr, fmin, n_bins, bins_per_octave, tuning,
tuning=tuning,
filter_scale=filter_scale,
norm=norm,
pad_fft=True)
pad_fft=True,
window=window)

# Filters are padded up to the nearest integral power of 2
n_fft = basis.shape[1]

if hop_length is not None and n_fft < 2.0**(1 + np.ceil(np.log2(hop_length))):
if (hop_length is not None and
n_fft < 2.0**(1 + np.ceil(np.log2(hop_length)))):

n_fft = int(2.0 ** (1 + np.ceil(np.log2(hop_length))))

# re-normalize bases with respect to the FFT window length
Expand Down Expand Up @@ -529,10 +552,12 @@ def __early_downsample(y, sr, hop_length, res_type, n_octaves,
raise ParameterError('Input signal length={:d} is too short for '
'{:d}-octave CQT'.format(len(y), n_octaves))

# The additional scaling of sqrt(downsample_factor) here is to implicitly
# rescale the filters
y = np.sqrt(downsample_factor) * audio.resample(y, sr, sr / downsample_factor,
res_type=res_type, scale=True)
# The additional scaling of sqrt(downsample_factor) here is to
# implicitly rescale the filters
y = np.sqrt(downsample_factor) * audio.resample(y, sr,
sr / downsample_factor,
res_type=res_type,
scale=True)

sr /= downsample_factor

Expand Down

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