Source code for librosa.effects

#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Effects
=======

Harmonic-percussive source separation
-------------------------------------
.. autosummary::
    :toctree: generated/

    hpss
    harmonic
    percussive

Time and frequency
------------------
.. autosummary::
    :toctree: generated/

    time_stretch
    pitch_shift

Miscellaneous
-------------
.. autosummary::
    :toctree: generated/

    remix
    trim
    split
    preemphasis
    deemphasis
"""

import numpy as np
import scipy.signal

from . import core
from . import decompose
from . import feature
from . import util
from .util.exceptions import ParameterError
from .util.decorators import deprecate_positional_args

__all__ = [
    "hpss",
    "harmonic",
    "percussive",
    "time_stretch",
    "pitch_shift",
    "remix",
    "trim",
    "split",
]


[docs]def hpss(y, **kwargs): """Decompose an audio time series into harmonic and percussive components. This function automates the STFT->HPSS->ISTFT pipeline, and ensures that the output waveforms have equal length to the input waveform ``y``. Parameters ---------- y : np.ndarray [shape=(..., n)] audio time series. Multi-channel is supported. **kwargs : additional keyword arguments. See `librosa.decompose.hpss` for details. Returns ------- y_harmonic : np.ndarray [shape=(..., n)] audio time series of the harmonic elements y_percussive : np.ndarray [shape=(..., n)] audio time series of the percussive elements See Also -------- harmonic : Extract only the harmonic component percussive : Extract only the percussive component librosa.decompose.hpss : HPSS on spectrograms Examples -------- >>> # Extract harmonic and percussive components >>> y, sr = librosa.load(librosa.ex('choice')) >>> y_harmonic, y_percussive = librosa.effects.hpss(y) >>> # Get a more isolated percussive component by widening its margin >>> y_harmonic, y_percussive = librosa.effects.hpss(y, margin=(1.0,5.0)) """ # Compute the STFT matrix stft = core.stft(y) # Decompose into harmonic and percussives stft_harm, stft_perc = decompose.hpss(stft, **kwargs) # Invert the STFTs. Adjust length to match the input. y_harm = core.istft(stft_harm, dtype=y.dtype, length=y.shape[-1]) y_perc = core.istft(stft_perc, dtype=y.dtype, length=y.shape[-1]) return y_harm, y_perc
[docs]def harmonic(y, **kwargs): """Extract harmonic elements from an audio time-series. Parameters ---------- y : np.ndarray [shape=(..., n)] audio time series. Multi-channel is supported. **kwargs : additional keyword arguments. See `librosa.decompose.hpss` for details. Returns ------- y_harmonic : np.ndarray [shape=(..., n)] audio time series of just the harmonic portion See Also -------- hpss : Separate harmonic and percussive components percussive : Extract only the percussive component librosa.decompose.hpss : HPSS for spectrograms Examples -------- >>> # Extract harmonic component >>> y, sr = librosa.load(librosa.ex('choice')) >>> y_harmonic = librosa.effects.harmonic(y) >>> # Use a margin > 1.0 for greater harmonic separation >>> y_harmonic = librosa.effects.harmonic(y, margin=3.0) """ # Compute the STFT matrix stft = core.stft(y) # Remove percussives stft_harm = decompose.hpss(stft, **kwargs)[0] # Invert the STFTs y_harm = core.istft(stft_harm, dtype=y.dtype, length=y.shape[-1]) return y_harm
[docs]def percussive(y, **kwargs): """Extract percussive elements from an audio time-series. Parameters ---------- y : np.ndarray [shape=(..., n)] audio time series. Multi-channel is supported. **kwargs : additional keyword arguments. See `librosa.decompose.hpss` for details. Returns ------- y_percussive : np.ndarray [shape=(..., n)] audio time series of just the percussive portion See Also -------- hpss : Separate harmonic and percussive components harmonic : Extract only the harmonic component librosa.decompose.hpss : HPSS for spectrograms Examples -------- >>> # Extract percussive component >>> y, sr = librosa.load(librosa.ex('choice')) >>> y_percussive = librosa.effects.percussive(y) >>> # Use a margin > 1.0 for greater percussive separation >>> y_percussive = librosa.effects.percussive(y, margin=3.0) """ # Compute the STFT matrix stft = core.stft(y) # Remove harmonics stft_perc = decompose.hpss(stft, **kwargs)[1] # Invert the STFT y_perc = core.istft(stft_perc, dtype=y.dtype, length=y.shape[-1]) return y_perc
[docs]@deprecate_positional_args def time_stretch(y, *, rate, **kwargs): """Time-stretch an audio series by a fixed rate. Parameters ---------- y : np.ndarray [shape=(..., n)] audio time series. Multi-channel is supported. rate : float > 0 [scalar] Stretch factor. If ``rate > 1``, then the signal is sped up. If ``rate < 1``, then the signal is slowed down. **kwargs : additional keyword arguments. See `librosa.decompose.stft` for details. Returns ------- y_stretch : np.ndarray [shape=(..., round(n/rate))] audio time series stretched by the specified rate See Also -------- pitch_shift : pitch shifting librosa.phase_vocoder : spectrogram phase vocoder pyrubberband.pyrb.time_stretch : high-quality time stretching using RubberBand Examples -------- Compress to be twice as fast >>> y, sr = librosa.load(librosa.ex('choice')) >>> y_fast = librosa.effects.time_stretch(y, rate=2.0) Or half the original speed >>> y_slow = librosa.effects.time_stretch(y, rate=0.5) """ if rate <= 0: raise ParameterError("rate must be a positive number") # Construct the short-term Fourier transform (STFT) stft = core.stft(y, **kwargs) # Stretch by phase vocoding stft_stretch = core.phase_vocoder( stft, rate=rate, hop_length=kwargs.get("hop_length", None), n_fft=kwargs.get("n_fft", None), ) # Predict the length of y_stretch len_stretch = int(round(y.shape[-1] / rate)) # Invert the STFT y_stretch = core.istft(stft_stretch, dtype=y.dtype, length=len_stretch, **kwargs) return y_stretch
[docs]@deprecate_positional_args def pitch_shift( y, *, sr, n_steps, bins_per_octave=12, res_type="kaiser_best", **kwargs ): """Shift the pitch of a waveform by ``n_steps`` steps. A step is equal to a semitone if ``bins_per_octave`` is set to 12. Parameters ---------- y : np.ndarray [shape=(..., n)] audio time series. Multi-channel is supported. sr : number > 0 [scalar] audio sampling rate of ``y`` n_steps : float [scalar] how many (fractional) steps to shift ``y`` bins_per_octave : float > 0 [scalar] how many steps per octave res_type : string Resample type. By default, 'kaiser_best' is used. See `librosa.resample` for more information. **kwargs : additional keyword arguments. See `librosa.decompose.stft` for details. Returns ------- y_shift : np.ndarray [shape=(..., n)] The pitch-shifted audio time-series See Also -------- time_stretch : time stretching librosa.phase_vocoder : spectrogram phase vocoder pyrubberband.pyrb.pitch_shift : high-quality pitch shifting using RubberBand Examples -------- Shift up by a major third (four steps if ``bins_per_octave`` is 12) >>> y, sr = librosa.load(librosa.ex('choice')) >>> y_third = librosa.effects.pitch_shift(y, sr=sr, n_steps=4) Shift down by a tritone (six steps if ``bins_per_octave`` is 12) >>> y_tritone = librosa.effects.pitch_shift(y, sr=sr, n_steps=-6) Shift up by 3 quarter-tones >>> y_three_qt = librosa.effects.pitch_shift(y, sr=sr, n_steps=3, ... bins_per_octave=24) """ if bins_per_octave < 1 or not np.issubdtype(type(bins_per_octave), np.integer): raise ParameterError("bins_per_octave must be a positive integer.") rate = 2.0 ** (-float(n_steps) / bins_per_octave) # Stretch in time, then resample y_shift = core.resample( time_stretch(y, rate=rate, **kwargs), orig_sr=float(sr) / rate, target_sr=sr, res_type=res_type, ) # Crop to the same dimension as the input return util.fix_length(y_shift, size=y.shape[-1])
[docs]@deprecate_positional_args def remix(y, intervals, *, align_zeros=True): """Remix an audio signal by re-ordering time intervals. Parameters ---------- y : np.ndarray [shape=(..., t)] Audio time series. Multi-channel is supported. intervals : iterable of tuples (start, end) An iterable (list-like or generator) where the ``i``th item ``intervals[i]`` indicates the start and end (in samples) of a slice of ``y``. align_zeros : boolean If ``True``, interval boundaries are mapped to the closest zero-crossing in ``y``. If ``y`` is stereo, zero-crossings are computed after converting to mono. Returns ------- y_remix : np.ndarray [shape=(..., d)] ``y`` remixed in the order specified by ``intervals`` Examples -------- Load in the example track and reverse the beats >>> y, sr = librosa.load(librosa.ex('choice')) Compute beats >>> _, beat_frames = librosa.beat.beat_track(y=y, sr=sr, ... hop_length=512) Convert from frames to sample indices >>> beat_samples = librosa.frames_to_samples(beat_frames) Generate intervals from consecutive events >>> intervals = librosa.util.frame(beat_samples, frame_length=2, ... hop_length=1).T Reverse the beat intervals >>> y_out = librosa.effects.remix(y, intervals[::-1]) """ y_out = [] if align_zeros: y_mono = core.to_mono(y) zeros = np.nonzero(core.zero_crossings(y_mono))[-1] # Force end-of-signal onto zeros zeros = np.append(zeros, [len(y_mono)]) for interval in intervals: if align_zeros: interval = zeros[util.match_events(interval, zeros)] y_out.append(y[..., interval[0] : interval[1]]) return np.concatenate(y_out, axis=-1)
def _signal_to_frame_nonsilent( y, frame_length=2048, hop_length=512, top_db=60, ref=np.max, aggregate=np.max ): """Frame-wise non-silent indicator for audio input. This is a helper function for `trim` and `split`. Parameters ---------- y : np.ndarray Audio signal, mono or stereo frame_length : int > 0 The number of samples per frame hop_length : int > 0 The number of samples between frames top_db : number > 0 The threshold (in decibels) below reference to consider as silence ref : callable or float The reference amplitude aggregate : callable [default: np.max] Function to aggregate dB measurements across channels (if y.ndim > 1) Note: for multiple leading axes, this is performed using ``np.apply_over_axes``. Returns ------- non_silent : np.ndarray, shape=(m,), dtype=bool Indicator of non-silent frames """ # Compute the MSE for the signal mse = feature.rms(y=y, frame_length=frame_length, hop_length=hop_length) # Convert to decibels and slice out the mse channel db = core.amplitude_to_db(mse[..., 0, :], ref=ref, top_db=None) # Aggregate everything but the time dimension if db.ndim > 1: db = np.apply_over_axes(aggregate, db, range(db.ndim - 1)) # Squeeze out leading singleton dimensions here # We always want to keep the trailing dimension though db = np.squeeze(db, axis=tuple(range(db.ndim - 1))) return db > -top_db
[docs]@deprecate_positional_args def trim( y, *, top_db=60, ref=np.max, frame_length=2048, hop_length=512, aggregate=np.max ): """Trim leading and trailing silence from an audio signal. Parameters ---------- y : np.ndarray, shape=(..., n) Audio signal. Multi-channel is supported. top_db : number > 0 The threshold (in decibels) below reference to consider as silence ref : number or callable The reference amplitude. By default, it uses `np.max` and compares to the peak amplitude in the signal. frame_length : int > 0 The number of samples per analysis frame hop_length : int > 0 The number of samples between analysis frames aggregate : callable [default: np.max] Function to aggregate across channels (if y.ndim > 1) Returns ------- y_trimmed : np.ndarray, shape=(..., m) The trimmed signal index : np.ndarray, shape=(2,) the interval of ``y`` corresponding to the non-silent region: ``y_trimmed = y[index[0]:index[1]]`` (for mono) or ``y_trimmed = y[:, index[0]:index[1]]`` (for stereo). Examples -------- >>> # Load some audio >>> y, sr = librosa.load(librosa.ex('choice')) >>> # Trim the beginning and ending silence >>> yt, index = librosa.effects.trim(y) >>> # Print the durations >>> print(librosa.get_duration(y), librosa.get_duration(yt)) 25.025986394557822 25.007891156462584 """ non_silent = _signal_to_frame_nonsilent( y, frame_length=frame_length, hop_length=hop_length, ref=ref, top_db=top_db, aggregate=aggregate, ) nonzero = np.flatnonzero(non_silent) if nonzero.size > 0: # Compute the start and end positions # End position goes one frame past the last non-zero start = int(core.frames_to_samples(nonzero[0], hop_length=hop_length)) end = min( y.shape[-1], int(core.frames_to_samples(nonzero[-1] + 1, hop_length=hop_length)), ) else: # The signal only contains zeros start, end = 0, 0 # Build the mono/stereo index full_index = [slice(None)] * y.ndim full_index[-1] = slice(start, end) return y[tuple(full_index)], np.asarray([start, end])
[docs]@deprecate_positional_args def split( y, *, top_db=60, ref=np.max, frame_length=2048, hop_length=512, aggregate=np.max ): """Split an audio signal into non-silent intervals. Parameters ---------- y : np.ndarray, shape=(..., n) An audio signal. Multi-channel is supported. top_db : number > 0 The threshold (in decibels) below reference to consider as silence ref : number or callable The reference amplitude. By default, it uses `np.max` and compares to the peak amplitude in the signal. frame_length : int > 0 The number of samples per analysis frame hop_length : int > 0 The number of samples between analysis frames aggregate : callable [default: np.max] Function to aggregate across channels (if y.ndim > 1) Returns ------- intervals : np.ndarray, shape=(m, 2) ``intervals[i] == (start_i, end_i)`` are the start and end time (in samples) of non-silent interval ``i``. """ non_silent = _signal_to_frame_nonsilent( y, frame_length=frame_length, hop_length=hop_length, ref=ref, top_db=top_db, aggregate=aggregate, ) # Interval slicing, adapted from # https://stackoverflow.com/questions/2619413/efficiently-finding-the-interval-with-non-zeros-in-scipy-numpy-in-python # Find points where the sign flips edges = np.flatnonzero(np.diff(non_silent.astype(int))) # Pad back the sample lost in the diff edges = [edges + 1] # If the first frame had high energy, count it if non_silent[0]: edges.insert(0, [0]) # Likewise for the last frame if non_silent[-1]: edges.append([len(non_silent)]) # Convert from frames to samples edges = core.frames_to_samples(np.concatenate(edges), hop_length=hop_length) # Clip to the signal duration edges = np.minimum(edges, y.shape[-1]) # Stack the results back as an ndarray return edges.reshape((-1, 2))
[docs]@deprecate_positional_args def preemphasis(y, *, coef=0.97, zi=None, return_zf=False): """Pre-emphasize an audio signal with a first-order differencing filter: y[n] -> y[n] - coef * y[n-1] Parameters ---------- y : np.ndarray [shape=(..., n)] Audio signal. Multi-channel is supported. coef : positive number Pre-emphasis coefficient. Typical values of ``coef`` are between 0 and 1. At the limit ``coef=0``, the signal is unchanged. At ``coef=1``, the result is the first-order difference of the signal. The default (0.97) matches the pre-emphasis filter used in the HTK implementation of MFCCs [#]_. .. [#] http://htk.eng.cam.ac.uk/ zi : number Initial filter state. When making successive calls to non-overlapping frames, this can be set to the ``zf`` returned from the previous call. (See example below.) By default ``zi`` is initialized as ``2*y[0] - y[1]``. return_zf : boolean If ``True``, return the final filter state. If ``False``, only return the pre-emphasized signal. Returns ------- y_out : np.ndarray pre-emphasized signal zf : number if ``return_zf=True``, the final filter state is also returned Examples -------- Apply a standard pre-emphasis filter >>> import matplotlib.pyplot as plt >>> y, sr = librosa.load(librosa.ex('trumpet')) >>> y_filt = librosa.effects.preemphasis(y) >>> # and plot the results for comparison >>> S_orig = librosa.amplitude_to_db(np.abs(librosa.stft(y)), ref=np.max, top_db=None) >>> S_preemph = librosa.amplitude_to_db(np.abs(librosa.stft(y_filt)), ref=np.max, top_db=None) >>> fig, ax = plt.subplots(nrows=2, sharex=True, sharey=True) >>> librosa.display.specshow(S_orig, y_axis='log', x_axis='time', ax=ax[0]) >>> ax[0].set(title='Original signal') >>> ax[0].label_outer() >>> img = librosa.display.specshow(S_preemph, y_axis='log', x_axis='time', ax=ax[1]) >>> ax[1].set(title='Pre-emphasized signal') >>> fig.colorbar(img, ax=ax, format="%+2.f dB") Apply pre-emphasis in pieces for block streaming. Note that the second block initializes ``zi`` with the final state ``zf`` returned by the first call. >>> y_filt_1, zf = librosa.effects.preemphasis(y[:1000], return_zf=True) >>> y_filt_2, zf = librosa.effects.preemphasis(y[1000:], zi=zf, return_zf=True) >>> np.allclose(y_filt, np.concatenate([y_filt_1, y_filt_2])) True See Also -------- deemphasis """ b = np.asarray([1.0, -coef], dtype=y.dtype) a = np.asarray([1.0], dtype=y.dtype) if zi is None: # Initialize the filter to implement linear extrapolation zi = 2 * y[..., 0:1] - y[..., 1:2] zi = np.atleast_1d(zi) y_out, z_f = scipy.signal.lfilter(b, a, y, zi=np.asarray(zi, dtype=y.dtype)) if return_zf: return y_out, z_f return y_out
[docs]@deprecate_positional_args def deemphasis(y, *, coef=0.97, zi=None, return_zf=False): """De-emphasize an audio signal with the inverse operation of preemphasis(): If y = preemphasis(x, coef=coef, zi=zi), the deemphasis is: >>> x[i] = y[i] + coef * x[i-1] >>> x = deemphasis(y, coef=coef, zi=zi) Parameters ---------- y : np.ndarray [shape=(..., n)] Audio signal. Multi-channel is supported. coef : positive number Pre-emphasis coefficient. Typical values of ``coef`` are between 0 and 1. At the limit ``coef=0``, the signal is unchanged. At ``coef=1``, the result is the first-order difference of the signal. The default (0.97) matches the pre-emphasis filter used in the HTK implementation of MFCCs [#]_. .. [#] http://htk.eng.cam.ac.uk/ zi : number Initial filter state. If inverting a previous preemphasis(), the same value should be used. By default ``zi`` is initialized as ``((2 - coef) * y[0] - y[1]) / (3 - coef)``. This value corresponds to the transformation of the default initialization of ``zi`` in ``preemphasis()``, ``2*x[0] - x[1]``. return_zf : boolean If ``True``, return the final filter state. If ``False``, only return the pre-emphasized signal. Returns ------- y_out : np.ndarray de-emphasized signal zf : number if ``return_zf=True``, the final filter state is also returned Examples -------- Apply a standard pre-emphasis filter and invert it with de-emphasis >>> y, sr = librosa.load(librosa.ex('trumpet')) >>> y_filt = librosa.effects.preemphasis(y) >>> y_deemph = librosa.effects.deemphasis(y_filt) >>> np.allclose(y, y_deemph) True See Also -------- preemphasis """ b = np.array([1.0, -coef], dtype=y.dtype) a = np.array([1.0], dtype=y.dtype) if zi is None: # initialize with all zeros zi = np.zeros(list(y.shape[:-1]) + [1], dtype=y.dtype) y_out, zf = scipy.signal.lfilter(a, b, y, zi=zi) # factor in the linear extrapolation y_out -= ( ((2 - coef) * y[..., 0:1] - y[..., 1:2]) / (3 - coef) * (coef ** np.arange(y.shape[-1])) ) else: zi = np.atleast_1d(zi) y_out, zf = scipy.signal.lfilter(a, b, y, zi=zi.astype(y.dtype)) if return_zf: return y_out, zf else: return y_out