}KgE_*dZddlZddlZddlmZddlmZddlm Z ddl m Z ddl m Z mZdd lmZdd lmZdd lmZdd lmZmZmZdd lmZgdZdddddddej8ddeej:dedeej:dedede dedeedej:fdZ!dddddddddeej:dedeej:dedede dedej:fd Z"ed!"dddddd#d$d%d&ejFdd' deej:dedeej:d(eej:ded)ed*ed+ed,eed-eed.efd/eejHjJdej:fd0Z&ed1"dddddddd#d$d&ddddddd2dej8d3deej:dedeej:d(eej:d4eej:deded)ed*ed,eed5eej:d6eej:d-eed.efd/eejHjJde ded7e'd8edeedej:f(d9Z(y):zRhythmic feature extractionN)util)cache) autocorrelate)stft)tempo_frequenciestime_to_frames) f0_harmonics)ParameterError) get_window)OptionalCallableAny) _WindowSpec) tempogramfourier_tempogramtempotempogram_ratioi"ViiThannysronset_envelope hop_length win_lengthcenterwindownormrrrrrrrrreturncddlm}|dkr tdt||d} || td|||| }|jd } |rI|jD cgc]} d } } t |dzfdz| d <t j|| d d d g}tj||d} |r | dd| f} tj| | jd} tjt| | zd|dScc} w)uCompute the tempogram: local autocorrelation of the onset strength envelope. [#]_ .. [#] Grosche, Peter, Meinard Müller, and Frank Kurth. "Cyclic tempogram - A mid-level tempo representation for music signals." ICASSP, 2010. 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 (..., m, n)] or None Optional pre-computed onset strength envelope as provided by `librosa.onset.onset_strength`. If multi-dimensional, tempograms are computed independently for each band (first dimension). hop_length : int > 0 number of audio samples between successive onset measurements win_length : int > 0 length of the onset autocorrelation window (in frames/onset measurements) The default settings (384) corresponds to ``384 * hop_length / sr ~= 8.9s``. center : bool If `True`, onset autocorrelation windows are centered. If `False`, windows are left-aligned. window : string, function, number, tuple, or np.ndarray [shape=(win_length,)] A window specification as in `stft`. norm : {np.inf, -np.inf, 0, float > 0, None} Normalization mode. Set to `None` to disable normalization. Returns ------- tempogram : np.ndarray [shape=(..., win_length, n)] Localized autocorrelation of the onset strength envelope. If given multi-band input (``onset_envelope.shape==(m,n)``) then ``tempogram[i]`` is the tempogram of ``onset_envelope[i]``. Raises ------ ParameterError if neither ``y`` nor ``onset_envelope`` are provided if ``win_length < 1`` See Also -------- fourier_tempogram librosa.onset.onset_strength librosa.util.normalize librosa.stft Examples -------- >>> # Compute local onset autocorrelation >>> y, sr = librosa.load(librosa.ex('nutcracker'), duration=30) >>> hop_length = 512 >>> oenv = librosa.onset.onset_strength(y=y, sr=sr, hop_length=hop_length) >>> tempogram = librosa.feature.tempogram(onset_envelope=oenv, sr=sr, ... hop_length=hop_length) >>> # Compute global onset autocorrelation >>> ac_global = librosa.autocorrelate(oenv, max_size=tempogram.shape[0]) >>> ac_global = librosa.util.normalize(ac_global) >>> # Estimate the global tempo for display purposes >>> tempo = librosa.feature.tempo(onset_envelope=oenv, sr=sr, ... hop_length=hop_length)[0] >>> import matplotlib.pyplot as plt >>> fig, ax = plt.subplots(nrows=4, figsize=(10, 10)) >>> times = librosa.times_like(oenv, sr=sr, hop_length=hop_length) >>> ax[0].plot(times, oenv, label='Onset strength') >>> ax[0].label_outer() >>> ax[0].legend(frameon=True) >>> librosa.display.specshow(tempogram, sr=sr, hop_length=hop_length, >>> x_axis='time', y_axis='tempo', cmap='magma', ... ax=ax[1]) >>> ax[1].axhline(tempo, color='w', linestyle='--', alpha=1, ... label='Estimated tempo={:g}'.format(tempo)) >>> ax[1].legend(loc='upper right') >>> ax[1].set(title='Tempogram') >>> x = np.linspace(0, tempogram.shape[0] * float(hop_length) / sr, ... num=tempogram.shape[0]) >>> ax[2].plot(x, np.mean(tempogram, axis=1), label='Mean local autocorrelation') >>> ax[2].plot(x, ac_global, '--', alpha=0.75, label='Global autocorrelation') >>> ax[2].set(xlabel='Lag (seconds)') >>> ax[2].legend(frameon=True) >>> freqs = librosa.tempo_frequencies(tempogram.shape[0], hop_length=hop_length, sr=sr) >>> ax[3].semilogx(freqs[1:], np.mean(tempogram[1:], axis=1), ... label='Mean local autocorrelation', base=2) >>> ax[3].semilogx(freqs[1:], ac_global[1:], '--', alpha=0.75, ... label='Global autocorrelation', base=2) >>> ax[3].axvline(tempo, color='black', linestyle='--', alpha=.8, ... label='Estimated tempo={:g}'.format(tempo)) >>> ax[3].legend(frameon=True) >>> ax[3].set(xlabel='BPM') >>> ax[3].grid(True) ronset_strength%win_length must be a positive integerT)fftbinsN+Either y or onset_envelope must be providedrrr)rr linear_rampr)mode end_values) frame_lengthr.ndimaxesaxis)rr2)onsetr"r r shapeintnppadrframe expand_tor/ normalizer)rrrrrrrrr" ac_windown_padding odf_frames Z/home/alanp/www/video.onchill/myenv/lib/python3.12/site-packages/librosa/feature/rhythm.pyrrs"h'A~DEE6:t qQIc2A2g& yy~~BGI >>i)+"5Dr #9s D)rrrrrrrcddlm}|dkr td|| td||||}t||d||S)u Compute the Fourier tempogram: the short-time Fourier transform of the onset strength envelope. [#]_ .. [#] Grosche, Peter, Meinard Müller, and Frank Kurth. "Cyclic tempogram - A mid-level tempo representation for music signals." ICASSP, 2010. 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 as provided by ``librosa.onset.onset_strength``. Multi-channel is supported. hop_length : int > 0 number of audio samples between successive onset measurements win_length : int > 0 length of the onset window (in frames/onset measurements) The default settings (384) corresponds to ``384 * hop_length / sr ~= 8.9s``. center : bool If `True`, onset windows are centered. If `False`, windows are left-aligned. window : string, function, number, tuple, or np.ndarray [shape=(win_length,)] A window specification as in `stft`. Returns ------- tempogram : np.ndarray [shape=(..., win_length // 2 + 1, n)] Complex short-time Fourier transform of the onset envelope. Raises ------ ParameterError if neither ``y`` nor ``onset_envelope`` are provided if ``win_length < 1`` See Also -------- tempogram librosa.onset.onset_strength librosa.util.normalize librosa.stft Examples -------- >>> # Compute local onset autocorrelation >>> y, sr = librosa.load(librosa.ex('nutcracker')) >>> hop_length = 512 >>> oenv = librosa.onset.onset_strength(y=y, sr=sr, hop_length=hop_length) >>> tempogram = librosa.feature.fourier_tempogram(onset_envelope=oenv, sr=sr, ... hop_length=hop_length) >>> # Compute the auto-correlation tempogram, unnormalized to make comparison easier >>> ac_tempogram = librosa.feature.tempogram(onset_envelope=oenv, sr=sr, ... hop_length=hop_length, norm=None) >>> import matplotlib.pyplot as plt >>> fig, ax = plt.subplots(nrows=3, sharex=True) >>> ax[0].plot(librosa.times_like(oenv), oenv, label='Onset strength') >>> ax[0].legend(frameon=True) >>> ax[0].label_outer() >>> librosa.display.specshow(np.abs(tempogram), sr=sr, hop_length=hop_length, >>> x_axis='time', y_axis='fourier_tempo', cmap='magma', ... ax=ax[1]) >>> ax[1].set(title='Fourier tempogram') >>> ax[1].label_outer() >>> librosa.display.specshow(ac_tempogram, sr=sr, hop_length=hop_length, >>> x_axis='time', y_axis='tempo', cmap='magma', ... ax=ax[2]) >>> ax[2].set(title='Autocorrelation tempogram') rr!r#r$r&r')n_fftrrr)r3r"r r)rrrrrrrr"s r@rrsZh'A~DEE 9 !NO O'!zJ jQvf )levelxg?g @gt@) rrrtgr start_bpmstd_bpmac_size max_tempo aggregatepriorrGrHrIrJrKrL.rMc |dkr td|-t|||j} t||||| }n|jd} | | |dd }|Jt | || } | 5d t j| t j|z |z d zz} n| j| } |5tt j| |k}t j | d|tj| |jd } t jt jd|z| zd}t j | |}|S)acEstimate the tempo (beats per minute) Parameters ---------- y : np.ndarray [shape=(..., n)] or None audio time series. Multi-channel is supported. sr : number > 0 [scalar] sampling rate of the time series onset_envelope : np.ndarray [shape=(..., n)] pre-computed onset strength envelope tg : np.ndarray pre-computed tempogram. If provided, then `y` and `onset_envelope` are ignored, and `win_length` is inferred from the shape of the tempogram. hop_length : int > 0 [scalar] hop length of the time series start_bpm : float [scalar] initial guess of the BPM std_bpm : float > 0 [scalar] standard deviation of tempo distribution ac_size : float > 0 [scalar] length (in seconds) of the auto-correlation window max_tempo : float > 0 [scalar, optional] If provided, only estimate tempo below this threshold aggregate : callable [optional] Aggregation function for estimating global tempo. If `None`, then tempo is estimated independently for each frame. prior : scipy.stats.rv_continuous [optional] A prior distribution over tempo (in beats per minute). By default, a pseudo-log-normal prior is used. If given, ``start_bpm`` and ``std_bpm`` will be ignored. Returns ------- tempo : np.ndarray estimated tempo (beats per minute). If input is multi-channel, one tempo estimate per channel is provided. See Also -------- librosa.onset.onset_strength librosa.feature.tempogram Notes ----- This function caches at level 30. Examples -------- >>> # Estimate a static tempo >>> y, sr = librosa.load(librosa.ex('nutcracker'), duration=30) >>> onset_env = librosa.onset.onset_strength(y=y, sr=sr) >>> tempo = librosa.feature.tempo(onset_envelope=onset_env, sr=sr) >>> tempo array([143.555]) >>> # Or a static tempo with a uniform prior instead >>> import scipy.stats >>> prior = scipy.stats.uniform(30, 300) # uniform over 30-300 BPM >>> utempo = librosa.feature.tempo(onset_envelope=onset_env, sr=sr, prior=prior) >>> utempo array([161.499]) >>> # Or a dynamic tempo >>> dtempo = librosa.feature.tempo(onset_envelope=onset_env, sr=sr, ... aggregate=None) >>> dtempo array([ 89.103, 89.103, 89.103, ..., 123.047, 123.047, 123.047]) >>> # Dynamic tempo with a proper log-normal prior >>> prior_lognorm = scipy.stats.lognorm(loc=np.log(120), scale=120, s=1) >>> dtempo_lognorm = librosa.feature.tempo(onset_envelope=onset_env, sr=sr, ... aggregate=None, ... prior=prior_lognorm) >>> dtempo_lognorm array([ 89.103, 89.103, 89.103, ..., 123.047, 123.047, 123.047]) Plot the estimated tempo against the onset autocorrelation >>> import matplotlib.pyplot as plt >>> # Convert to scalar >>> tempo = tempo.item() >>> utempo = utempo.item() >>> # Compute 2-second windowed autocorrelation >>> hop_length = 512 >>> ac = librosa.autocorrelate(onset_env, max_size=2 * sr // hop_length) >>> freqs = librosa.tempo_frequencies(len(ac), sr=sr, ... hop_length=hop_length) >>> # Plot on a BPM axis. We skip the first (0-lag) bin. >>> fig, ax = plt.subplots() >>> ax.semilogx(freqs[1:], librosa.util.normalize(ac)[1:], ... label='Onset autocorrelation', base=2) >>> ax.axvline(tempo, 0, 1, alpha=0.75, linestyle='--', color='r', ... label='Tempo (default prior): {:.2f} BPM'.format(tempo)) >>> ax.axvline(utempo, 0, 1, alpha=0.75, linestyle=':', color='g', ... label='Tempo (uniform prior): {:.2f} BPM'.format(utempo)) >>> ax.set(xlabel='Tempo (BPM)', title='Static tempo estimation') >>> ax.grid(True) >>> ax.legend() Plot dynamic tempo estimates over a tempogram >>> fig, ax = plt.subplots() >>> tg = librosa.feature.tempogram(onset_envelope=onset_env, sr=sr, ... hop_length=hop_length) >>> librosa.display.specshow(tg, x_axis='time', y_axis='tempo', cmap='magma', ax=ax) >>> ax.plot(librosa.times_like(dtempo), dtempo, ... color='c', linewidth=1.5, label='Tempo estimate (default prior)') >>> ax.plot(librosa.times_like(dtempo_lognorm), dtempo_lognorm, ... color='c', linewidth=1.5, linestyle='--', ... label='Tempo estimate (lognorm prior)') >>> ax.set(title='Dynamic tempo estimation') >>> ax.legend() rz#start_bpm must be strictly positiveN)rr)rrrrrr-r(T)r2keepdims)rrgrr.g.Ar1)r r itemrr4rr6log2logpdfr5argmaxinfrr9r/log1ptake)rrrrGrrHrIrJrKrLrMrbpmslogpriormax_idx best_period tempo_ests r@rrsMBA~BCC z#GzJOOQ )!!  XXb\  rT 2 >> ZJ2 FD }BGGDMBGGI,>>'IaOO<<%biiy 012 ffW'~~hRWW2>H))BHHS2X.9CKGGD+6I rC(linear)rrrrGbpmrrrHrIrKfreqsfactorsrLrMrrkind fill_valuerr^r_r`rarbc |t||||||||}| t|t||} |t|||||| d| }| t j gd} t || || ||}| | |dS|S) aTempogram ratio features, also known as spectral rhythm patterns. [1]_ This function summarizes the energy at metrically important multiples of the tempo. For example, if the tempo corresponds to the quarter-note period, the tempogram ratio will measure the energy at the eighth note, sixteenth note, half note, whole note, etc. periods, as well as dotted and triplet ratios. By default, the multiplicative factors used here are as specified by [2]_. If the estimated tempo corresponds to a quarter note, these factors will measure relative energy at the following metrical subdivisions: +-------+--------+------------------+ | Index | Factor | Description | +=======+========+==================+ | 0 | 4 | Sixteenth note | +-------+--------+------------------+ | 1 | 8/3 | Dotted sixteenth | +-------+--------+------------------+ | 2 | 3 | Eighth triplet | +-------+--------+------------------+ | 3 | 2 | Eighth note | +-------+--------+------------------+ | 4 | 4/3 | Dotted eighth | +-------+--------+------------------+ | 5 | 3/2 | Quarter triplet | +-------+--------+------------------+ | 6 | 1 | Quarter note | +-------+--------+------------------+ | 7 | 2/3 | Dotted quarter | +-------+--------+------------------+ | 8 | 3/4 | Half triplet | +-------+--------+------------------+ | 9 | 1/2 | Half note | +-------+--------+------------------+ | 10 | 1/3 | Dotted half note | +-------+--------+------------------+ | 11 | 3/8 | Whole triplet | +-------+--------+------------------+ | 12 | 1/4 | Whole note | +-------+--------+------------------+ .. [1] Peeters, Geoffroy. "Rhythm Classification Using Spectral Rhythm Patterns." In ISMIR, pp. 644-647. 2005. .. [2] Prockup, Matthew, Andreas F. Ehmann, Fabien Gouyon, Erik M. Schmidt, and Youngmoo E. Kim. "Modeling musical rhythm at scale with the music genome project." In 2015 IEEE workshop on applications of signal processing to audio and acoustics (WASPAA), pp. 1-5. IEEE, 2015. Parameters ---------- y : np.ndarray [shape=(..., n)] or None audio time series sr : number > 0 [scalar] sampling rate of the time series onset_envelope : np.ndarray [shape=(..., n)] pre-computed onset strength envelope tg : np.ndarray pre-computed tempogram. If provided, then `y` and `onset_envelope` are ignored, and `win_length` is inferred from the shape of the tempogram. bpm : np.ndarray pre-computed tempo estimate. This must be a per-frame estimate, and have dimension compatible with `tg`. hop_length : int > 0 [scalar] hop length of the time series win_length : int > 0 [scalar] window length of the autocorrelation window for tempogram calculation start_bpm : float [scalar] initial guess of the BPM if `bpm` is not provided std_bpm : float > 0 [scalar] standard deviation of tempo distribution max_tempo : float > 0 [scalar, optional] If provided, only estimate tempo below this threshold freqs : np.ndarray Frequencies (in BPM) of the tempogram axis. factors : np.ndarray Multiples of the fundamental tempo (bpm) to estimate. If not provided, the factors are as specified above. prior : scipy.stats.rv_continuous [optional] A prior distribution over tempo (in beats per minute). By default, a pseudo-log-normal prior is used. If given, ``start_bpm`` and ``std_bpm`` will be ignored. center : bool If `True`, onset windows are centered. If `False`, windows are left-aligned. aggregate : callable [optional] Aggregation function for estimating global tempogram ratio. If `None`, then ratios are estimated independently for each frame. window : string, function, number, tuple, or np.ndarray [shape=(win_length,)] A window specification as in `stft`. kind : str Interpolation mode for measuring tempogram ratios fill_value : float The value to fill when extrapolating beyond the observed frequency range. norm : {np.inf, -np.inf, 0, float > 0, None} Normalization mode. Set to `None` to disable normalization. Returns ------- tgr : np.ndarray The tempogram ratio for the specified factors. If `aggregate` is provided, the trailing time axis will be removed. If `aggregate` is not provided (default), ratios will be estimated for each frame. See Also -------- tempogram tempo librosa.f0_harmonics librosa.tempo_frequencies Examples -------- Compute tempogram ratio features using the default factors for a waltz (3/4 time) >>> import matplotlib.pyplot as plt >>> y, sr = librosa.load(librosa.ex('sweetwaltz')) >>> tempogram = librosa.feature.tempogram(y=y, sr=sr) >>> tgr = librosa.feature.tempogram_ratio(tg=tempogram, sr=sr) >>> fig, ax = plt.subplots(nrows=2, sharex=True) >>> librosa.display.specshow(tempogram, x_axis='time', y_axis='tempo', ... ax=ax[0]) >>> librosa.display.specshow(tgr, x_axis='time', ax=ax[1]) >>> ax[0].label_outer() >>> ax[0].set(title="Tempogram") >>> ax[1].set(title="Tempogram ratio") Nr)rn_binsr)rrGrrHrIrKrLrM) gUUUUUU@rgUUUUUU?g?r#gUUUUUU?g?g?gUUUUUU?g?g?)r_f0 harmonicsrarbr(r1)rrlenrr6arrayr )rrrrGr^rrrHrIrKr_r`rLrMrrrarbrtgrs r@rrs| z )!!   }!RBJO {!  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