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You're reading the documentation for a development version. For the latest released version, please have a look at 0.9.1.

librosa.iirt

librosa.iirt(y, *, sr=22050, win_length=2048, hop_length=None, center=True, tuning=0.0, pad_mode='constant', flayout='sos', res_type='kaiser_best', **kwargs)[source]

Time-frequency representation using IIR filters

This function will return a time-frequency representation using a multirate filter bank consisting of IIR filters. 1

First, y is resampled as needed according to the provided sample_rates.

Then, a filterbank with with n band-pass filters is designed.

The resampled input signals are processed by the filterbank as a whole. (scipy.signal.filtfilt resp. sosfiltfilt is used to make the phase linear.) The output of the filterbank is cut into frames. For each band, the short-time mean-square power (STMSP) is calculated by summing win_length subsequent filtered time samples.

When called with the default set of parameters, it will generate the TF-representation (pitch filterbank):

  • 85 filters with MIDI pitches [24, 108] as center_freqs.

  • each filter having a bandwidth of one semitone.

1

Müller, Meinard. “Information Retrieval for Music and Motion.” Springer Verlag. 2007.

Parameters
ynp.ndarray [shape=(…, n)]

audio time series. Multi-channel is supported.

srnumber > 0 [scalar]

sampling rate of y

win_lengthint > 0, <= n_fft

Window length.

hop_lengthint > 0 [scalar]

Hop length, number samples between subsequent frames. If not supplied, defaults to win_length // 4.

centerboolean

  • If True, the signal y is padded so that frame D[..., :, t] is centered at y[t * hop_length].

  • If False, then D[…, :, t]` begins at y[t * hop_length]

tuningfloat [scalar]

Tuning deviation from A440 in fractions of a bin.

pad_modestring

If center=True, the padding mode to use at the edges of the signal. By default, this function uses zero padding.

flayoutstring

  • If sos (default), a series of second-order filters is used for filtering with scipy.signal.sosfiltfilt. Minimizes numerical precision errors for high-order filters, but is slower.

  • If ba, the standard difference equation is used for filtering with scipy.signal.filtfilt. Can be unstable for high-order filters.

res_typestring

The resampling mode. See librosa.resample for details.

**kwargsadditional keyword arguments

Additional arguments for librosa.filters.semitone_filterbank (e.g., could be used to provide another set of center_freqs and sample_rates).

Returns
bands_powernp.ndarray [shape=(…, n, t), dtype=dtype]

Short-time mean-square power for the input signal.

Raises
ParameterError

If flayout is not None, ba, or sos.

See also

librosa.filters.semitone_filterbank
librosa.filters.mr_frequencies
librosa.cqt
scipy.signal.filtfilt
scipy.signal.sosfiltfilt

Examples

>>> import matplotlib.pyplot as plt
>>> y, sr = librosa.load(librosa.ex('trumpet'), duration=3)
>>> D = np.abs(librosa.iirt(y))
>>> C = np.abs(librosa.cqt(y=y, sr=sr))
>>> fig, ax = plt.subplots(nrows=2, sharex=True, sharey=True)
>>> img = librosa.display.specshow(librosa.amplitude_to_db(C, ref=np.max),
...                                y_axis='cqt_hz', x_axis='time', ax=ax[0])
>>> ax[0].set(title='Constant-Q transform')
>>> ax[0].label_outer()
>>> img = librosa.display.specshow(librosa.amplitude_to_db(D, ref=np.max),
...                                y_axis='cqt_hz', x_axis='time', ax=ax[1])
>>> ax[1].set_title('Semitone spectrogram (iirt)')
>>> fig.colorbar(img, ax=ax, format="%+2.0f dB")
../_images/librosa-iirt-1.png