U jg,b@s\dZddlZddlZddlZddlZddlmZddlm Z ddlm Z ddlm Z ddl m Z mZdd l mZdd lmZdd lmZmZdd lmZmZmZmZmZdd lmZdddgZeddddeZdddddddddddd eeje eeje!e e e"eeeejfeej#j$e%e"eeeejfejfd ddZ&ddddddd dd!eeje eeje!e!ee ee eej#j$ejd" d#dZ'ejeje e e"ejd$d%d&Z(d'd(Z)ej*d)d*gd+dd,d-d.d/Z+ej*d0d1gd2ddd-d3d4Z,ej*d5d6gd7ddd-d8d9Z-d:d;Z.ej*dddd-d?d@Z/ej*dAdBgdCddd-dDdEZ0dS)Fz^ Beat and tempo ============== .. autosummary:: :toctree: generated/ beat_track plp N)cache)core)onset)util) tempogramfourier_tempogram)tempo)ParameterError)moved vectorize)AnyCallableOptionalTupleUnion) _FloatLike_co beat_trackr plpzlibrosa.beat.tempoz0.10.0z1.0)Z moved_fromversionZversion_removedi"Vig^@dTframes) ysronset_envelope hop_length start_bpm tightnesstrimbpmpriorunitssparse) rrrrrrrrr r!r"returnc CsP|dkr,|dkrtdtj|||tjd}| rL|jdkrLtd|jd|s| rjdtjgtdfStj |j dd t d tj |t dfS|dkrt|||||d }t|} tj| |jt| jd } t|| t ||||} | rHt| } | d krnH| dkr|tj| |dfS| dkr:|tj| ||dfStd| || fS)a Dynamic programming beat tracker. Beats are detected in three stages, following the method of [#]_: 1. Measure onset strength 2. Estimate tempo from onset correlation 3. Pick peaks in onset strength approximately consistent with estimated tempo .. [#] Ellis, Daniel PW. "Beat tracking by dynamic programming." Journal of New Music Research 36.1 (2007): 51-60. http://labrosa.ee.columbia.edu/projects/beattrack/ Parameters ---------- y : np.ndarray [shape=(..., n)] or None audio time series sr : number > 0 [scalar] sampling rate of ``y`` onset_envelope : np.ndarray [shape=(..., m)] or None (optional) pre-computed onset strength envelope. hop_length : int > 0 [scalar] number of audio samples between successive ``onset_envelope`` values start_bpm : float > 0 [scalar] initial guess for the tempo estimator (in beats per minute) tightness : float [scalar] tightness of beat distribution around tempo trim : bool [scalar] trim leading/trailing beats with weak onsets bpm : float [scalar] or np.ndarray [shape=(...)] (optional) If provided, use ``bpm`` as the tempo instead of estimating it from ``onsets``. If multichannel, tempo estimates can be provided for all channels. Tempo estimates may also be time-varying, in which case the shape of ``bpm`` should match that of ``onset_envelope``, i.e., one estimate provided for each frame. prior : scipy.stats.rv_continuous [optional] An optional prior distribution over tempo. If provided, ``start_bpm`` will be ignored. units : {'frames', 'samples', 'time'} The units to encode detected beat events in. By default, 'frames' are used. sparse : bool If ``True`` (default), detections are returned as an array of frames, samples, or time indices (as specified by ``units=``). If ``False``, detections are encoded as a dense boolean array where ``beats[..., n]`` is true if there's a beat at frame index ``n``. .. note:: multi-channel input is only supported when ``sparse=False``. Returns ------- tempo : float [scalar, non-negative] or np.ndarray estimated global tempo (in beats per minute) If multi-channel and ``bpm`` is not provided, a separate tempo will be returned for each channel beats : np.ndarray estimated beat event locations. If `sparse=True` (default), beat locations are given in the specified units (default is frame indices). If `sparse=False` (required for multichannel input), beat events are indicated by a boolean for each frame. .. note:: If no onset strength could be detected, beat_tracker estimates 0 BPM and returns an empty list. Raises ------ ParameterError if neither ``y`` nor ``onset_envelope`` are provided, or if ``units`` is not one of 'frames', 'samples', or 'time' See Also -------- librosa.onset.onset_strength Examples -------- Track beats using time series input >>> y, sr = librosa.load(librosa.ex('choice'), duration=10) >>> tempo, beats = librosa.beat.beat_track(y=y, sr=sr) >>> tempo 135.99917763157896 Print the frames corresponding to beats >>> beats array([ 3, 21, 40, 59, 78, 96, 116, 135, 154, 173, 192, 211, 230, 249, 268, 287, 306, 325, 344, 363]) Or print them as timestamps >>> librosa.frames_to_time(beats, sr=sr) array([0.07 , 0.488, 0.929, 1.37 , 1.811, 2.229, 2.694, 3.135, 3.576, 4.017, 4.458, 4.899, 5.341, 5.782, 6.223, 6.664, 7.105, 7.546, 7.988, 8.429]) Output beat detections as a boolean array instead of frame indices >>> tempo, beats_dense = librosa.beat.beat_track(y=y, sr=sr, sparse=False) >>> beats_dense array([False, False, False, True, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, ..., False, False, True, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False]) Track beats using a pre-computed onset envelope >>> onset_env = librosa.onset.onset_strength(y=y, sr=sr, ... aggregate=np.median) >>> tempo, beats = librosa.beat.beat_track(onset_envelope=onset_env, ... sr=sr) >>> tempo 135.99917763157896 >>> beats array([ 3, 21, 40, 59, 78, 96, 116, 135, 154, 173, 192, 211, 230, 249, 268, 287, 306, 325, 344, 363]) Plot the beat events against the onset strength envelope >>> import matplotlib.pyplot as plt >>> hop_length = 512 >>> fig, ax = plt.subplots(nrows=2, sharex=True) >>> times = librosa.times_like(onset_env, sr=sr, hop_length=hop_length) >>> M = librosa.feature.melspectrogram(y=y, sr=sr, hop_length=hop_length) >>> librosa.display.specshow(librosa.power_to_db(M, ref=np.max), ... y_axis='mel', x_axis='time', hop_length=hop_length, ... ax=ax[0]) >>> ax[0].label_outer() >>> ax[0].set(title='Mel spectrogram') >>> ax[1].plot(times, librosa.util.normalize(onset_env), ... label='Onset strength') >>> ax[1].vlines(times[beats], 0, 1, alpha=0.5, color='r', ... linestyle='--', label='Beats') >>> ax[1].legend() Nz$y or onset_envelope must be providedrrrZ aggregaterz'sparse=True (default) does not support zK-dimensional inputs. Either set sparse=False or convert the signal to mono.dtypeshaper')rrrrr ndimZaxesrZsamples)rtime)rrzInvalid unit type: )r ronset_strengthnpmedianr,anyarrayintzerosr*float zeros_likebool_tempoZ atleast_1dr expand_torange__beat_trackerZ flatnonzerorZframes_to_samplesZframes_to_time)rrrrrrrrr r!r"Z_bpmZ bpm_expandedbeatsr=0/tmp/pip-unpacked-wheel-756mdtiw/librosa/beat.pyr%sT+      ii,)rrrr win_length tempo_min tempo_maxr ) rrrrr@rArBr r#c CsV|dkrtj|||tjd}|dk rH|dk rH||krHtd|d|t||||d}tj|||d} |dk rd|d| |kddf<|dk rd|d| |kddf<tj | |j d d } t d t |} |dk r| | | 7} | jd d d } d|| | k<|t|dt |jd d d }tj|d||jdd} t| dd| } tj| ddS)aPredominant local pulse (PLP) estimation. [#]_ The PLP method analyzes the onset strength envelope in the frequency domain to find a locally stable tempo for each frame. These local periodicities are used to synthesize local half-waves, which are combined such that peaks coincide with rhythmically salient frames (e.g. onset events on a musical time grid). The local maxima of the pulse curve can be taken as estimated beat positions. This method may be preferred over the dynamic programming method of `beat_track` when the tempo is expected to vary significantly over time. Additionally, since `plp` does not require the entire signal to make predictions, it may be preferable when beat-tracking long recordings in a streaming setting. .. [#] Grosche, P., & Muller, M. (2011). "Extracting predominant local pulse information from music recordings." IEEE Transactions on Audio, Speech, and Language Processing, 19(6), 1688-1701. Parameters ---------- y : np.ndarray [shape=(..., n)] or None audio time series. Multi-channel is supported. sr : number > 0 [scalar] sampling rate of ``y`` onset_envelope : np.ndarray [shape=(..., n)] or None (optional) pre-computed onset strength envelope hop_length : int > 0 [scalar] number of audio samples between successive ``onset_envelope`` values win_length : int > 0 [scalar] number of frames to use for tempogram analysis. By default, 384 frames (at ``sr=22050`` and ``hop_length=512``) corresponds to about 8.9 seconds. tempo_min, tempo_max : numbers > 0 [scalar], optional Minimum and maximum permissible tempo values. ``tempo_max`` must be at least ``tempo_min``. Set either (or both) to `None` to disable this constraint. prior : scipy.stats.rv_continuous [optional] A prior distribution over tempo (in beats per minute). By default, a uniform prior over ``[tempo_min, tempo_max]`` is used. Returns ------- pulse : np.ndarray, shape=[(..., n)] The estimated pulse curve. Maxima correspond to rhythmically salient points of time. If input is multi-channel, one pulse curve per channel is computed. See Also -------- beat_track librosa.onset.onset_strength librosa.feature.fourier_tempogram Examples -------- Visualize the PLP compared to an onset strength envelope. Both are normalized here to make comparison easier. >>> y, sr = librosa.load(librosa.ex('brahms')) >>> onset_env = librosa.onset.onset_strength(y=y, sr=sr) >>> pulse = librosa.beat.plp(onset_envelope=onset_env, sr=sr) >>> # Or compute pulse with an alternate prior, like log-normal >>> import scipy.stats >>> prior = scipy.stats.lognorm(loc=np.log(120), scale=120, s=1) >>> pulse_lognorm = librosa.beat.plp(onset_envelope=onset_env, sr=sr, ... prior=prior) >>> melspec = librosa.feature.melspectrogram(y=y, sr=sr) >>> import matplotlib.pyplot as plt >>> fig, ax = plt.subplots(nrows=3, sharex=True) >>> librosa.display.specshow(librosa.power_to_db(melspec, ... ref=np.max), ... x_axis='time', y_axis='mel', ax=ax[0]) >>> ax[0].set(title='Mel spectrogram') >>> ax[0].label_outer() >>> ax[1].plot(librosa.times_like(onset_env), ... librosa.util.normalize(onset_env), ... label='Onset strength') >>> ax[1].plot(librosa.times_like(pulse), ... librosa.util.normalize(pulse), ... label='Predominant local pulse (PLP)') >>> ax[1].set(title='Uniform tempo prior [30, 300]') >>> ax[1].label_outer() >>> ax[2].plot(librosa.times_like(onset_env), ... librosa.util.normalize(onset_env), ... label='Onset strength') >>> ax[2].plot(librosa.times_like(pulse_lognorm), ... librosa.util.normalize(pulse_lognorm), ... label='Predominant local pulse (PLP)') >>> ax[2].set(title='Log-normal tempo prior, mean=120', xlim=[5, 20]) >>> ax[2].legend() PLP local maxima can be used as estimates of beat positions. >>> tempo, beats = librosa.beat.beat_track(onset_envelope=onset_env) >>> beats_plp = np.flatnonzero(librosa.util.localmax(pulse)) >>> import matplotlib.pyplot as plt >>> fig, ax = plt.subplots(nrows=2, sharex=True, sharey=True) >>> times = librosa.times_like(onset_env, sr=sr) >>> ax[0].plot(times, librosa.util.normalize(onset_env), ... label='Onset strength') >>> ax[0].vlines(times[beats], 0, 1, alpha=0.5, color='r', ... linestyle='--', label='Beats') >>> ax[0].legend() >>> ax[0].set(title='librosa.beat.beat_track') >>> ax[0].label_outer() >>> # Limit the plot to a 15-second window >>> times = librosa.times_like(pulse, sr=sr) >>> ax[1].plot(times, librosa.util.normalize(pulse), ... label='PLP') >>> ax[1].vlines(times[beats_plp], 0, 1, alpha=0.5, color='r', ... linestyle='--', label='PLP Beats') >>> ax[1].legend() >>> ax[1].set(title='librosa.beat.plp', xlim=[5, 20]) >>> ax[1].xaxis.set_major_formatter(librosa.display.TimeFormatter()) Nr$z tempo_max=z must be larger than tempo_min=)rrrr@)rrr@r.r+g.AT)axiskeepdims?rr()rZn_fftlengthrD)rr.r/r0r rrZfourier_tempo_frequenciesrr9r,log1pabsZlogpdfmaxtinyZistftr*Zclip normalize) rrrrr@rArBr ZftgramZtempo_frequenciesZftmagZ peak_valuesZpulser=r=r>rsT &)rr frame_raterrr#c Cst|dkrtd|d|dkr.td|jdd|jdfkr^td|jd|jt|d |}tt||}t|||\}}t|} tj |t d } t || | t || |} | S) a[Tracks beats in an onset strength envelope. Parameters ---------- onset_envelope : np.ndarray [shape=(..., n,)] onset strength envelope bpm : float [scalar] or np.ndarray [shape=(...)] tempo estimate frame_rate : float [scalar] frame rate of the spectrogram (sr / hop_length, frames per second) tightness : float [scalar, positive] how closely do we adhere to bpm? trim : bool [scalar] trim leading/trailing beats with weak onsets? Returns ------- beats : np.ndarray [shape=(n,)] frame numbers of beat events rzbpm=z must be strictly positivez#tightness must be strictly positiver(rzInvalid bpm shape=z% does not match onset envelope shape=gN@r&) r/r1r r*round__beat_local_score__normalize_onsets__beat_track_dp __last_beatr6r7__dp_backtrack __trim_beats) rrrNrrframes_per_beat localscorebacklinkcumscoretailr<r=r=r>r;s  r;cCs"|jdddd}||t|S)z2Normalize onset strength by its standard deviationrr(T)ZddofrDrE)ZstdrrL)ZonsetsZnormr=r=r>rQsrQz(void(float32[:], float32[:], float32[:])z(void(float64[:], float64[:], float64[:])z (t),(n)->(t)F)Znopythonrc Cst|}t|dkrtdt|d |ddd|dd}t|}tt|D]h}d||<ttd||d|dt||d|D],}|||||||d|7<qq\nt|t|krtt|D]}tdt|| ||dd||d}dt||d}d||<ttd||d|dt||d|D].}|||||||d|7<qhqdS)Nrgrg@@r%)lenr/expZaranger:rKminr3)rrVrWNZwindowKikr=r=r>rPs  42.42rPz;void(float32[:], float32[:], float32, int32[:], float32[:])z;void(float64[:], float64[:], float32, int32[:], float64[:])z(t),(n),()->(t),(t)c Cs"d|}d}d|d<|d|d<tt|dk}t|D]\}} tj } d} t|t|||d|d|||ddD]P} | dkrq|| |t|| t|||d} | | kr| } | } q| dkr| | ||<n| ||<|r| |krd||<q<| ||<d}qrR(s,  :0 rRz+void(float32[:], bool_[:], bool_, bool_[:])z+void(float64[:], bool_[:], bool_, bool_[:])z(t),(t),()->(t)cCs||dd<td}t|||t|dt|t|d}|r`d|dd}nd}d}|||krd||<|d7}qht|d}|||krd||<|d8}qdS) zCRemove spurious leading and trailing beats from the detection arrayNr[rFr%rFr)r/ZhanningZconvolver\Zmean)rWr<rZ beats_trimmedwZ smooth_boe thresholdnr=r=r>rU[s  0     rUcCsjtj|dd}tjj||d}tjj|dd}dtj|}tj|jddt d}t |||||S)z/Identify the position of the last detected beatr(rH)datamaskrFNr)) rZlocalmaxr/maZ masked_arrayr0Zgetdataemptyr*r3__last_beat_selector)rYrmZ masked_scoresZmediansZ thresholdsrZr=r=r>rSsrSz-void(float32[:], bool_[:], float32, int64[:])z-void(float64[:], bool_[:], float64, int64[:])z(t),(t),()->()cCsJt|d}||d<|dkrF||s<|||kr<||d<qFq|d8}qdS)zmVectorized helper to identify the last valid beat position: cumscore[n] > threshold and not mask[n] rrN)r\)rYrmrjoutrkr=r=r>rps rpzvoid(int32[:], int32, bool_[:])zvoid(int64[:], int64, bool_[:])z (t),()->(t)cCs"|}|dkrd||<||}qdS)z>Populate the beat indicator array from a sequence of backlinksrTNr=)Z backlinksrZr<rkr=r=r>rTs rT)1__doc__Znumpyr/ZscipyZ scipy.statsZnumba_cacherrrrZfeaturerrr r8Zutil.exceptionsr Zutil.decoratorsr r typingr rrrr_typingr__all__Zndarrayr5r3r7statsZ rv_continuousstrrrr;rQZ guvectorizerPrRrUrSrprTr=r=r=r>s            c  ? 7 " ,