o a&i>]@sdZddlZddlZddlmZddlmZddlmZddlm Z ddl m Z dd l m Z mZmZmZmZmZmZdd lmZdd lmZgd Zd ejde deejejffddZd ejde dejfddZd ejde dejfddZd ejdede dejfddZdddd ejdededede de dejfd d!Z!d"d#d ejd$eeeefd%e"dejfd&d'Z#d(d)d*ej$ej$fd ejd+ed,ed-ed.eeefd/edejfd0d1Z%d*ej$d(d)ej$d2d ejd-ed.eeefd+ed,ed/edeejejffd3d4Z&d*ej$d(d)ej$d2d ejd-ed.eeefd+ed,ed/edejfd5d6Z'ed7d7d7d8d ejd9ed:eed;ed<dejf d=d>Z(ed7d7d?d ejd9ed:eed;ed"deejejff d@d>Z(ed7d7d?d ejd9ed:eed;e"deejeejejfff dAd>Z(dBddZ(ed7d7d7d8d ejd9ed:eed;ed<dejf dDdEZ)ed7d7d?d ejd9ed:eed;ed"deejejff dFdEZ)dBddHPSS->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)) dtypelengthrstftrristftrshape)rrr! stft_harm stft_percy_harmy_percr(:C:\wamp64\www\opt\env\Lib\site-packages\librosa/effects.pyr<s &rcK<t|}tj|fi|d}tj||j|jdd}|S)akExtract 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) rrrr )rrr!r$r&r(r(r)rn !rcKr*)ayExtract 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) rrrr )rrr!r%r'r(r(r)rr+rratecKsx|dkrtdtj|fi|}tj|||dd|ddd}tt|jd|}tj|f|j |d|}|S) aTime-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) rzrate must be a positive number hop_lengthNn_fft)r,r-r.rr) rrr!Z phase_vocodergetintroundr#r"r)rr,rr!Z stft_stretchZ len_stretchZ y_stretchr(r(r)rs(  r Zsoxr_hq)bins_per_octaveres_typesrn_stepsr3r4cKsjt|s td|ddt| |}tjt|fd|i|t||||d}tj||jddS)aShift 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, 'soxr_hq' 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) zbins_per_octave=z must be a positive integer.g@r,)Zorig_srZ target_srr4r)size) rZis_positive_intrfloatrZresamplerZ fix_lengthr#)rr5r6r3r4rr,Zy_shiftr(r(r)rs B  rT) align_zeros intervalsr9cCsg}|rt|}tt|d}t|t|g}|D]}|r*|t||}||d|d|dfqtj |ddS)a^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]) r.rrZaxis) rZto_mononpnonzeroZzero_crossingsappendlenrZ match_events concatenate)rr:r9y_outZy_monozerosintervalr(r(r)rZs0  rii< frame_lengthr-top_dbref aggregatecCsvtj|||d}tj|ddddf|dd}|jdkr6t||t|jd}tj|t t|jdd}|| kS)a?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 )rrEr-.rN)rGrFrr;) rZrmsrZamplitude_to_dbndimr<Zapply_over_axesrangeZsqueezetuple)rrEr-rFrGrHZmsedbr(r(r)_signal_to_frame_nonsilents *  rM)rFrGrEr-rHc Cst||||||d}t|}|jdkr3ttj|d|d}t|jdttj|dd|d} nd\}} t dg|j } t || | d<|t | t || gfS)aCTrim 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 rEr-rGrFrHrr-rr)rrN) rMr< flatnonzeror7r0rframes_to_samplesminr#slicerIrKasarray) rrFrGrEr-rH non_silentr=startendZ full_indexr(r(r)rs&0 rcCst||||||d}tt|t}|dg}|dr(|dtdg|dr7|tt |gt j t ||d}t ||jd}|d}|S)aoSplit 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``. rNrrrrO)r)rMr<rPdiffastyper0insertarrayr>r?rrQr@minimumr#Zreshape)rrFrGrEr-rHrUedgesr(r(r)r"s$$  r.)coefzi return_zfr_r`raFcCdSNr(rr_r`rar(r(r) preemphasisjre)r_r`cCrbrcr(rdr(r(r)reurfcCrbrcr(rdr(r(r)rerfg ףp= ?cCstjd| g|jd}tjdg|jd}|dur*d|dddf|dddf}t|}tjj|||tj||jdd\}}|rG||fS|S) a 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 ?rNrX.rrr`)r<rTr atleast_1dscipysignallfilter)rr_r`rabarAZz_fr(r(r)resL$ $cCrbrcr(rdr(r(r) deemphasisrfrpcCrbrcr(rdr(r(r)rprfcCstjd| g|jd}tjdg|jd}|dur[tjt|jdddg|jd}tjj||||d\}}|d||dd df|dddfd ||t |jd8}nt |}tjj|||| |jd\}}|rv||fS|S) aDe-emphasize an audio signal with the inverse operation of preemphasis(): If y = preemphasis(x, coef=coef, zi=zi), the deemphasis is: >>> x[i] = y[i] + coef * x[i-1] >>> x = deemphasis(y, coef=coef, zi=zi) Parameters ---------- y : np.ndarray [shape=(..., n)] Audio signal. Multi-channel is supported. coef : positive number Pre-emphasis coefficient. Typical values of ``coef`` are between 0 and 1. At the limit ``coef=0``, the signal is unchanged. At ``coef=1``, the result is the first-order difference of the signal. The default (0.97) matches the pre-emphasis filter used in the HTK implementation of MFCCs [#]_. .. [#] http://htk.eng.cam.ac.uk/ zi : number Initial filter state. If inverting a previous preemphasis(), the same value should be used. By default ``zi`` is initialized as ``((2 - coef) * y[0] - y[1]) / (3 - coef)``. This value corresponds to the transformation of the default initialization of ``zi`` in ``preemphasis()``, ``2*x[0] - x[1]``. return_zf : boolean If ``True``, return the final filter state. If ``False``, only return the pre-emphasized signal. Returns ------- y_out : np.ndarray de-emphasized signal zf : number if ``return_zf=True``, the final filter state is also returned Examples -------- Apply a standard pre-emphasis filter and invert it with de-emphasis >>> y, sr = librosa.load(librosa.ex('trumpet')) >>> y_filt = librosa.effects.preemphasis(y) >>> y_deemph = librosa.effects.deemphasis(y_filt) >>> np.allclose(y, y_deemph) True See Also -------- preemphasis rgrhNrrrirX.r) r<r\rrBlistr#rkrlrmZarangerjrZ)rr_r`rarnrorAzfr(r(r)rps"A$&  )*__doc__numpyr<Z scipy.signalrkrrrrZutil.exceptionsrtypingrr r r r r rZtyping_extensionsrZ numpy.typingr__all__Zndarrayrrrr8rr0strrboolrmaxrMrrrerpr(r(r(r)s!     $  $ 2,,D V C  <  Q  H       `