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, ...]) Computes 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]) Computes 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