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librosa.onset.onset_strength_multi

librosa.onset.onset_strength_multi(y=None, sr=22050, S=None, n_fft=2048, hop_length=512, lag=1, max_size=1, ref=None, detrend=False, center=True, feature=None, aggregate=None, channels=None, **kwargs)

Compute a spectral flux onset strength envelope across multiple channels.

Onset strength for channel i at time t is determined by:

mean_{f in channels[i]} max(0, S[f, t+1] - S[f, t])

Parameters:

ynp.ndarray [shape=(n,)]

audio time-series

srnumber > 0 [scalar]

sampling rate of y

Snp.ndarray [shape=(d, m)]

pre-computed (log-power) spectrogram

n_fftint > 0 [scalar]

FFT window size for use in feature() if S is not provided.

hop_lengthint > 0 [scalar]

hop length for use in feature() if S is not provided.

lagint > 0

time lag for computing differences

max_sizeint > 0

size (in frequency bins) of the local max filter. set to 1 to disable filtering.

refNone or np.ndarray [shape=(d, m)]

An optional pre-computed reference spectrum, of the same shape as S. If not provided, it will be computed from S. If provided, it will override any local max filtering governed by max_size.

detrendbool [scalar]

Filter the onset strength to remove the DC component

centerbool [scalar]

Shift the onset function by n_fft // (2 * hop_length) frames. This corresponds to using a centered frame analysis in the short-time Fourier transform.

featurefunction

Function for computing time-series features, eg, scaled spectrograms. By default, uses librosa.feature.melspectrogram with fmax=11025.0

Must support arguments: y, sr, n_fft, hop_length

aggregatefunction or False

Aggregation function to use when combining onsets at different frequency bins.

If False, then no aggregation is performed.

Default: np.mean

channelslist or None

Array of channel boundaries or slice objects. If None, then a single channel is generated to span all bands.

kwargsadditional keyword arguments

Additional parameters to feature(), if S is not provided.

Returns:

onset_envelopenp.ndarray [shape=(n_channels, m)]

array containing the onset strength envelope for each specified channel

Raises:

ParameterError

if neither (y, sr) nor S are provided

See also

onset_strength

Notes

This function caches at level 30.

Examples

First, load some audio and plot the spectrogram

>>> import matplotlib.pyplot as plt
>>> y, sr = librosa.load(librosa.ex('choice'), duration=5)
>>> D = np.abs(librosa.stft(y))
>>> fig, ax = plt.subplots(nrows=2, sharex=True)
>>> img1 = librosa.display.specshow(librosa.amplitude_to_db(D, ref=np.max),
...                          y_axis='log', x_axis='time', ax=ax[0])
>>> ax[0].set(title='Power spectrogram')
>>> ax[0].label_outer()
>>> fig.colorbar(img1, ax=[ax[0]], format="%+2.f dB")

Construct a standard onset function over four sub-bands

>>> onset_subbands = librosa.onset.onset_strength_multi(y=y, sr=sr,
...                                                     channels=[0, 32, 64, 96, 128])
>>> img2 = librosa.display.specshow(onset_subbands, x_axis='time', ax=ax[1])
>>> ax[1].set(ylabel='Sub-bands', title='Sub-band onset strength')
>>> fig.colorbar(img2, ax=[ax[1]])