U jg&l@sdZddlZddlZddlmZddlmZddlmZddlm Z ddl m Z dd l m Z mZmZmZmZmZmZmZdd lmZdd lmZdd lmZmZmZmZmZmZm Z m!Z!d dddddddgZ"ddddddddddd ej#ee ee e fee fe$e%ee!ee!e!fee!fe&ee&ee&ee%eeej#ej#fd dd Z'ddddddddddd ej#ee ee e fee fe$e%ee!ee!e!fee!fe&ee&ee&ee%eej#d d dZ(ddddddddddd ej#ee ee e fee fe$e%ee!ee!e!fee!fe&ee&ee&ee%eej#d d!dZ)ej#e$e ej#d"d#dZ*d$d%dd&ej#e$e$e&e+e%e ej#d'd(dZ,dd)ej#eee&e&fe%ej#d*d+dZ-dd,d-ej.ej.fej#e&e&e$eee$feej#d.d/d0Z/d-ej.dd,ej.d1ej#e$ee$efe&e&eeej#ej#fd2d3dZ0d-ej.dd,ej.d1ej#e$ee$efe&e&eej#d2d4dZ1ed5d5d5d6ej#e$eeedej#d7d8d9Z2ed5d5d:ej#e$eeedeej#ej#fd7d;d9Z2ed5d5d:ej#e$eee%eej#eej#ej#ffd7dd9Z2ed5d5d5d6ej#e$eeedej#d7d?d@Z3ed5d5d:ej#e$eeedeej#ej#fd7dAd@Z3d=ddd6ej#e$eee%eej#eej#ej#ffd7dBd@Z3dS)Ca 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 N)core) decompose)feature)util)ParameterError)AnyCallableIterableOptionalTupleListUnionoverload)Literal) ArrayLike) _WindowSpec_PadMode _PadModeSTFT _SequenceLike_ScalarOrSequence_ComplexLike_co _IntLike_co _FloatLike_cohpssharmonic percussive time_stretch pitch_shiftremixtrimsplit@F?iZhannTZconstant) kernel_sizepowermaskmarginn_fft hop_length win_lengthwindowcenterpad_mode) yr%r&r'r(r)r*r+r,r-r.returnc  Csvtj||||| | d} tj| ||||d\} } tj| |j|||| |jdd}tj| |j|||| |jdd}||fS)aDecompose 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. kernel_size power mask margin See `librosa.deocmpose.hpss` n_fft hop_length win_length window center pad_mode See `librosa.stft` 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)) r)r*r+r-r.r%r&r'r(dtyper)r*r+r-lengthrstftrristftr5shape)r/r%r&r'r(r)r*r+r,r-r.r8 stft_harm stft_percy_harmy_percr?3/tmp/pip-unpacked-wheel-756mdtiw/librosa/effects.pyrFsD?    c  CsRtj||||| | d} tj| ||||dd} tj| |j|||| |jdd} | S)aExtract harmonic elements from an audio time-series. Parameters ---------- y : np.ndarray [shape=(..., n)] audio time series. Multi-channel is supported. kernel_size power mask margin See `librosa.deocmpose.hpss` n_fft hop_length win_length window center pad_mode See `librosa.stft` 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) r1r2rr3r4r7)r/r%r&r'r(r)r*r+r,r-r.r8r;r=r?r?r@rs6:  c  CsRtj||||| | d} tj| ||||dd} tj| |j|||| |jdd} | S)aExtract percussive elements from an audio time-series. Parameters ---------- y : np.ndarray [shape=(..., n)] audio time series. Multi-channel is supported. kernel_size power mask margin See `librosa.deocmpose.hpss` n_fft hop_length win_length window center pad_mode See `librosa.stft` 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) r1r2rr3r4r7)r/r%r&r'r(r)r*r+r,r-r.r8r<r>r?r?r@rs6:  )r/ratekwargsr0cKst|dkrtdtj|f|}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 numberr*Nr))rAr*r)r3)r5r6) rrr8Z phase_vocodergetintroundr:r9r5)r/rArBr8Z stft_stretchZ len_stretchZ y_stretchr?r?r@rVs&   Zsoxr_hq)bins_per_octaveres_typescale)r/srn_stepsrGrHrIrBr0c Kslt|std|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 : int > 0 [scalar] how many steps per octave res_type : string Resample type. By default, 'soxr_hq' is used. See `librosa.resample` for more information. scale : bool Scale the resampled signal so that ``y`` and ``y_hat`` have approximately equal total energy. **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.r#rA)Zorig_srZ target_srrHrIr3)size) rZis_positive_intrfloatrZresamplerZ fix_lengthr:) r/rJrKrGrHrIrBrAZy_shiftr?r?r@rsF    ) align_zeros)r/ intervalsrNr0cCsg}|r8t|}tt|d}t|t|g}|D]6}|rT|t||}||d|d|dfq>> 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]) r3.rrZaxis) rZto_mononpnonzeroZzero_crossingsappendlenrZ match_events concatenate)r/rOrNy_outZy_monozerosintervalr?r?r@rs/  i<)r/ frame_lengthr*top_dbref aggregater0cCsvtj|||d}tj|ddddf|dd}|jdkrlt||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 )r/rZr*.rN)r\r[rrP) rZrmsrZamplitude_to_dbndimrQZapply_over_axesrangeZsqueezetuple)r/rZr*r[r\r]Zmsedbr?r?r@_signal_to_frame_nonsilent-s ) rb)r[r\rZr*r])r/r[r\rZr*r]r0c Cst||||||d}t|}|jdkrfttj|d|d}t|jdttj|dd|d} nd\}} t dg|j } t || | d<|t | t || gfS)aTrim leading and trailing silence from an audio signal. Silence is defined as segments of the audio signal that are `top_db` decibels (or more) quieter than a reference level, `ref`. By default, `ref` is set to the signal's maximum RMS value. It's important to note that if the entire signal maintains a uniform RMS value, there will be no segments considered quieter than the maximum, leading to no trimming. This implies that a completely silent signal will remain untrimmed with the default `ref` setting. In these situations, an explicit value for `ref` (in decibels) should be used instead. 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, sr=sr), librosa.get_duration(yt, sr=sr)) 25.025986394557822 25.007891156462584 rZr*r\r[r]rr*r3r)rrN) rbrQ flatnonzerorLrDrframes_to_samplesminr:slicer^r`asarray) r/r[r\rZr*r] non_silentrRstartendZ full_indexr?r?r@r es&8  cCst||||||d}tt|t}|dg}|drP|dtdg|drn|tt |gt j t ||d}t ||jd}|d}|S)anSplit 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``. rcrrr3rd)r3)rbrQreZdiffastyperDinsertarrayrSrTrrfrUZminimumr:Zreshape)r/r[r\rZr*r]rjedgesr?r?r@r!s$"   .)coefzi return_zf)r/rrrsrtr0cCsdSNr?r/rrrsrtr?r?r@ preemphasissrw)rrrscCsdSrur?rvr?r?r@rw scCsdSrur?rvr?r?r@rwsg ףp= ?cCstjd| g|jd}tjdg|jd}|dkrTd|dddf|dddf}t|}tjj|||tj||jdd\}}|r||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 [#]_. .. [#] https://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 r$r5Nrm.rrrs)rQrir5 atleast_1dscipysignallfilter)r/rrrsrtbarVZz_fr?r?r@rwsL$ $cCsdSrur?rvr?r?r@ deemphasissrcCsdSrur?rvr?r?r@rscCstjd| g|jd}tjdg|jd}|dkrtjt|jdddg|jd}tjj||||d\}}|d||dd df|dddfd ||t |jd8}n*t |}tjj|||| |jd\}}|r||fS|SdS) aDe-emphasize an audio signal with the inverse operation of preemphasis(): If y = preemphasis(x, coef=coef, zi=zi), the deemphasis is: >>> x[i] = y[i] + coef * x[i-1] >>> x = deemphasis(y, coef=coef, zi=zi) Parameters ---------- y : np.ndarray [shape=(..., n)] Audio signal. Multi-channel is supported. coef : positive number Pre-emphasis coefficient. Typical values of ``coef`` are between 0 and 1. At the limit ``coef=0``, the signal is unchanged. At ``coef=1``, the result is the first-order difference of the signal. The default (0.97) matches the pre-emphasis filter used in the HTK implementation of MFCCs [#]_. .. [#] https://htk.eng.cam.ac.uk/ zi : number Initial filter state. If inverting a previous preemphasis(), the same value should be used. By default ``zi`` is initialized as ``((2 - coef) * y[0] - y[1]) / (3 - coef)``. This value corresponds to the transformation of the default initialization of ``zi`` in ``preemphasis()``, ``2*x[0] - x[1]``. return_zf : boolean If ``True``, return the final filter state. If ``False``, only return the pre-emphasized signal. Returns ------- y_out : np.ndarray de-emphasized signal zf : number if ``return_zf=True``, the final filter state is also returned Examples -------- Apply a standard pre-emphasis filter and invert it with de-emphasis >>> y, sr = librosa.load(librosa.ex('trumpet')) >>> y_filt = librosa.effects.preemphasis(y) >>> y_deemph = librosa.effects.deemphasis(y_filt) >>> np.allclose(y, y_deemph) True See Also -------- preemphasis r$rxNr3rryrm.r) rQrpr5rWlistr:r{r|r}Zarangerzrn)r/rrrsrtr~rrVzfr?r?r@rs"@$&  )4__doc__ZnumpyrQZ scipy.signalr{rrrrZutil.exceptionsrtypingrr r r r r rrZtyping_extensionsrZ numpy.typingr_typingrrrrrrrr__all__ZndarrayrMboolrDrrrrstrrrmaxrbr r!rwrr?r?r?r@s!     (  (  i [ VB [ B  ;  Y  F    `