librosa.core.iirt

librosa.core.iirt(y, sr=22050, win_length=2048, hop_length=None, center=True, tuning=0.0, pad_mode=’reflect’, **kwargs)

Time-frequency representation using IIR filters [1].

This function will return a time-frequency representation using a multirate filter bank consisting of IIR filters. 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 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 as described in [1] (pitch filterbank):

• 85 filters with MIDI pitches [24, 108] as center_freqs.
• each filter having a bandwith of one semitone.

[1]	(1, 2) Müller, Meinard. “Information Retrieval for Music and Motion.” Springer Verlag. 2007.

Parameters:

y : np.ndarray [shape=(n,)]

audio time series

sr : number > 0 [scalar]

sampling rate of y

win_length : int > 0, <= n_fft

Window length.

hop_length : int > 0 [scalar]

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

center : boolean

• 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]

tuning : float in [-0.5, +0.5) [scalar]

Tuning deviation from A440 in fractions of a bin.

pad_mode : string

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

kwargs : additional 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_power : np.ndarray [shape=(n, t), dtype=dtype]

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

See also

librosa.filters.semitone_filterbank, librosa.filters._multirate_fb, librosa.filters.mr_frequencies, librosa.core.cqt, scipy.signal.filtfilt

Examples

>>> import matplotlib.pyplot as plt
>>> y, sr = librosa.load(librosa.util.example_audio_file())
>>> D = librosa.iirt(y)
>>> librosa.display.specshow(librosa.amplitude_to_db(D, ref=np.max),
...                          y_axis='cqt_hz', x_axis='time')
>>> plt.title('Semitone spectrogram')
>>> plt.colorbar(format='%+2.0f dB')
>>> plt.tight_layout()

(Source code)