Caution

You're reading the documentation for a development version. For the latest released version, please have a look at 0.10.2.

Feature extraction

Spectral features

chroma_stft(*[, y, sr, S, norm, n_fft, ...])	Compute a chromagram from a waveform or power spectrogram.
chroma_cqt(*[, y, sr, C, hop_length, fmin, ...])	Constant-Q chromagram
chroma_cens(*[, y, sr, C, hop_length, fmin, ...])	Compute the chroma variant "Chroma Energy Normalized" (CENS)
chroma_vqt(*[, y, sr, V, hop_length, fmin, ...])	Variable-Q chromagram
melspectrogram(*[, y, sr, S, n_fft, ...])	Compute a mel-scaled spectrogram.
mfcc(*[, y, sr, S, n_mfcc, dct_type, norm, ...])	Mel-frequency cepstral coefficients (MFCCs)
rms(*[, y, S, frame_length, hop_length, ...])	Compute root-mean-square (RMS) value for each frame, either from the audio samples y or from a spectrogram S.
spectral_centroid(*[, y, sr, S, n_fft, ...])	Compute the spectral centroid.
spectral_bandwidth(*[, y, sr, S, n_fft, ...])	Compute p'th-order spectral bandwidth.
spectral_contrast(*[, y, sr, S, n_fft, ...])	Compute spectral contrast
spectral_flatness(*[, y, S, n_fft, ...])	Compute spectral flatness
spectral_rolloff(*[, y, sr, S, n_fft, ...])	Compute roll-off frequency.
poly_features(*[, y, sr, S, n_fft, ...])	Get coefficients of fitting an nth-order polynomial to the columns of a spectrogram.
tonnetz(*[, y, sr, chroma])	Compute the tonal centroid features (tonnetz)
zero_crossing_rate(y, *[, frame_length, ...])	Compute the zero-crossing rate of an audio time series.

Rhythm features

tempo(*[, y, sr, onset_envelope, tg, ...])	Estimate the tempo (beats per minute)
tempogram(*[, y, sr, onset_envelope, ...])	Compute the tempogram: local autocorrelation of the onset strength envelope.
fourier_tempogram(*[, y, sr, ...])	Compute the Fourier tempogram: the short-time Fourier transform of the onset strength envelope.
tempogram_ratio(*[, y, sr, onset_envelope, ...])	Tempogram ratio features, also known as spectral rhythm patterns.

Feature manipulation

delta(data, *[, width, order, axis, mode])	Compute delta features: local estimate of the derivative of the input data along the selected axis.
stack_memory(data, *[, n_steps, delay])	Short-term history embedding: vertically concatenate a data vector or matrix with delayed copies of itself.

Feature inversion

inverse.mel_to_stft(M, *[, sr, n_fft, power])	Approximate STFT magnitude from a Mel power spectrogram.
inverse.mel_to_audio(M, *[, sr, n_fft, ...])	Invert a mel power spectrogram to audio using Griffin-Lim.
inverse.mfcc_to_mel(mfcc, *[, n_mels, ...])	Invert Mel-frequency cepstral coefficients to approximate a Mel power spectrogram.
inverse.mfcc_to_audio(mfcc, *[, n_mels, ...])	Convert Mel-frequency cepstral coefficients to a time-domain audio signal