Caution

You're reading an old version of this documentation. If you want up-to-date information, please have a look at 0.9.1.

Core IO and DSP

Audio processing

load(path[, sr, mono, offset, duration, ...])	Load an audio file as a floating point time series.
stream(path, block_length, frame_length, ...)	Stream audio in fixed-length buffers.
to_mono(y)	Force an audio signal down to mono by averaging samples across channels.
resample(y, orig_sr, target_sr[, res_type, ...])	Resample a time series from orig_sr to target_sr
get_duration([y, sr, S, n_fft, hop_length, ...])	Compute the duration (in seconds) of an audio time series, feature matrix, or filename.
get_samplerate(path)	Get the sampling rate for a given file.
autocorrelate(y[, max_size, axis])	Bounded auto-correlation
lpc(y, order)	Linear Prediction Coefficients via Burg's method
zero_crossings(y[, threshold, ...])	Find the zero-crossings of a signal y: indices i such that sign(y[i]) != sign(y[j]).
clicks([times, frames, sr, hop_length, ...])	Returns a signal with the signal click placed at each specified time
tone(frequency[, sr, length, duration, phi])	Returns a pure tone signal.
chirp(fmin, fmax[, sr, length, duration, ...])	Returns a chirp signal that goes from frequency fmin to frequency fmax
mu_compress(x[, mu, quantize])	mu-law compression
mu_expand(x[, mu, quantize])	mu-law expansion

Spectral representations

stft(y[, n_fft, hop_length, win_length, ...])	Short-time Fourier transform (STFT).
istft(stft_matrix[, hop_length, win_length, ...])	Inverse short-time Fourier transform (ISTFT).
reassigned_spectrogram(y[, sr, S, n_fft, ...])	Time-frequency reassigned spectrogram.
cqt(y[, sr, hop_length, fmin, n_bins, ...])	Compute the constant-Q transform of an audio signal.
icqt(C[, sr, hop_length, fmin, ...])	Compute the inverse constant-Q transform.
hybrid_cqt(y[, sr, hop_length, fmin, ...])	Compute the hybrid constant-Q transform of an audio signal.
pseudo_cqt(y[, sr, hop_length, fmin, ...])	Compute the pseudo constant-Q transform of an audio signal.
iirt(y[, sr, win_length, hop_length, ...])	Time-frequency representation using IIR filters [Rd4077732470d-1].
fmt(y[, t_min, n_fmt, kind, beta, ...])	The fast Mellin transform (FMT) [R6343f8d4cac9-1] of a uniformly sampled signal y.
griffinlim(S[, n_iter, hop_length, ...])	Approximate magnitude spectrogram inversion using the "fast" Griffin-Lim algorithm [R047f50301c96-1] [R047f50301c96-2].
griffinlim_cqt(C[, n_iter, sr, hop_length, ...])	Approximate constant-Q magnitude spectrogram inversion using the "fast" Griffin-Lim algorithm [Re33fb425db1f-1] [Re33fb425db1f-2].
interp_harmonics(x, freqs, h_range[, kind, ...])	Compute the energy at harmonics of time-frequency representation.
salience(S, freqs, h_range[, weights, ...])	Harmonic salience function.
phase_vocoder(D, rate[, hop_length])	Phase vocoder.
magphase(D[, power])	Separate a complex-valued spectrogram D into its magnitude (S) and phase (P) components, so that D = S * P.
get_fftlib()	Get the FFT library currently used by librosa
set_fftlib([lib])	Set the FFT library used by librosa.

Magnitude scaling

amplitude_to_db(S[, ref, amin, top_db])	Convert an amplitude spectrogram to dB-scaled spectrogram.
db_to_amplitude(S_db[, ref])	Convert a dB-scaled spectrogram to an amplitude spectrogram.
power_to_db(S[, ref, amin, top_db])	Convert a power spectrogram (amplitude squared) to decibel (dB) units
db_to_power(S_db[, ref])	Convert a dB-scale spectrogram to a power spectrogram.
perceptual_weighting(S, frequencies, **kwargs)	Perceptual weighting of a power spectrogram:
A_weighting(frequencies[, min_db])	Compute the A-weighting of a set of frequencies.
pcen(S[, sr, hop_length, gain, bias, power, ...])	Per-channel energy normalization (PCEN) [Rb388d53f6b92-1]

Time and frequency conversion

frames_to_samples(frames[, hop_length, n_fft])	Converts frame indices to audio sample indices.
frames_to_time(frames[, sr, hop_length, n_fft])	Converts frame counts to time (seconds).
samples_to_frames(samples[, hop_length, n_fft])	Converts sample indices into STFT frames.
samples_to_time(samples[, sr])	Convert sample indices to time (in seconds).
time_to_frames(times[, sr, hop_length, n_fft])	Converts time stamps into STFT frames.
time_to_samples(times[, sr])	Convert timestamps (in seconds) to sample indices.
blocks_to_frames(blocks, block_length)	Convert block indices to frame indices
blocks_to_samples(blocks, block_length, ...)	Convert block indices to sample indices
blocks_to_time(blocks, block_length, ...)	Convert block indices to time (in seconds)
hz_to_note(frequencies, **kwargs)	Convert one or more frequencies (in Hz) to the nearest note names.
hz_to_midi(frequencies)	Get MIDI note number(s) for given frequencies
midi_to_hz(notes)	Get the frequency (Hz) of MIDI note(s)
midi_to_note(midi[, octave, cents])	Convert one or more MIDI numbers to note strings.
note_to_hz(note, **kwargs)	Convert one or more note names to frequency (Hz)
note_to_midi(note[, round_midi])	Convert one or more spelled notes to MIDI number(s).
hz_to_mel(frequencies[, htk])	Convert Hz to Mels
hz_to_octs(frequencies[, tuning, ...])	Convert frequencies (Hz) to (fractional) octave numbers.
mel_to_hz(mels[, htk])	Convert mel bin numbers to frequencies
octs_to_hz(octs[, tuning, bins_per_octave, A440])	Convert octaves numbers to frequencies.
fft_frequencies([sr, n_fft])	Alternative implementation of np.fft.fftfreq
cqt_frequencies(n_bins, fmin[, ...])	Compute the center frequencies of Constant-Q bins.
mel_frequencies([n_mels, fmin, fmax, htk])	Compute an array of acoustic frequencies tuned to the mel scale.
tempo_frequencies(n_bins[, hop_length, sr])	Compute the frequencies (in beats per minute) corresponding to an onset auto-correlation or tempogram matrix.
fourier_tempo_frequencies([sr, win_length, ...])	Compute the frequencies (in beats per minute) corresponding to a Fourier tempogram matrix.
samples_like(X[, hop_length, n_fft, axis])	Return an array of sample indices to match the time axis from a feature matrix.
times_like(X[, sr, hop_length, n_fft, axis])	Return an array of time values to match the time axis from a feature matrix.

Pitch and tuning

estimate_tuning([y, sr, S, n_fft, ...])	Estimate the tuning of an audio time series or spectrogram input.
pitch_tuning(frequencies[, resolution, ...])	Given a collection of pitches, estimate its tuning offset (in fractions of a bin) relative to A440=440.0Hz.
piptrack([y, sr, S, n_fft, hop_length, ...])	Pitch tracking on thresholded parabolically-interpolated STFT.

Deprecated

ifgram(y[, sr, n_fft, hop_length, ...])

Compute the instantaneous frequency (as a proportion of the sampling rate) obtained as the time-derivative of the phase of the complex spectrum as described by [Ra44d590316d7-1].