librosa/beat.py
def beat_track(y=None, sr=22050, onset_envelope=None, hop_length=512,
               start_bpm=120.0, tightness=100, trim=True, bpm=None,
               units='frames'):
    r'''Dynamic programming beat tracker.

    Beats are detected in three stages, following the method of [1]_:
      1. Measure onset strength
      2. Estimate tempo from onset correlation
      3. Pick peaks in onset strength approximately consistent with estimated
         tempo

    .. [1] 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=(n,)] 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]
        (optional) If provided, use `bpm` as the tempo instead of
        estimating it from `onsets`.

    units : {'frames', 'samples', 'time'}
        The units to encode detected beat events in.
        By default, 'frames' are used.


    Returns
    -------

    tempo : float [scalar, non-negative]
        estimated global tempo (in beats per minute)

    beats : np.ndarray [shape=(m,)]
        estimated beat event locations in the specified units
        (default is frame indices)

    .. 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.util.example_audio_file())

    >>> tempo, beats = librosa.beat.beat_track(y=y, sr=sr)
    >>> tempo
    64.599609375


    Print the first 20 beat frames

    >>> beats[:20]
    array([ 320,  357,  397,  436,  480,  525,  569,  609,  658,
            698,  737,  777,  817,  857,  896,  936,  976, 1016,
           1055, 1095])


    Or print them as timestamps

    >>> librosa.frames_to_time(beats[:20], sr=sr)
    array([  7.43 ,   8.29 ,   9.218,  10.124,  11.146,  12.19 ,
            13.212,  14.141,  15.279,  16.208,  17.113,  18.042,
            18.971,  19.9  ,  20.805,  21.734,  22.663,  23.591,
            24.497,  25.426])


    Track beats using a pre-computed onset envelope

    >>> onset_env = librosa.onset.onset_strength(y, sr=sr,
    ...                                          aggregate=np.median)
    >>> tempo, beats = librosa.beat.beat_track(onset_envelope=onset_env,
    ...                                        sr=sr)
    >>> tempo
    64.599609375
    >>> beats[:20]
    array([ 320,  357,  397,  436,  480,  525,  569,  609,  658,
            698,  737,  777,  817,  857,  896,  936,  976, 1016,
           1055, 1095])


    Plot the beat events against the onset strength envelope

    >>> import matplotlib.pyplot as plt
    >>> hop_length = 512
    >>> plt.figure(figsize=(8, 4))
    >>> times = librosa.frames_to_time(np.arange(len(onset_env)),
    ...                                sr=sr, hop_length=hop_length)
    >>> plt.plot(times, librosa.util.normalize(onset_env),
    ...          label='Onset strength')
    >>> plt.vlines(times[beats], 0, 1, alpha=0.5, color='r',
    ...            linestyle='--', label='Beats')
    >>> plt.legend(frameon=True, framealpha=0.75)
    >>> # Limit the plot to a 15-second window
    >>> plt.xlim(15, 30)
    >>> plt.gca().xaxis.set_major_formatter(librosa.display.TimeFormatter())
    >>> plt.tight_layout()
    '''

    # First, get the frame->beat strength profile if we don't already have one
    if onset_envelope is None:
        if y is None:
            raise ParameterError('y or onset_envelope must be provided')

        onset_envelope = onset.onset_strength(y=y,
                                              sr=sr,
                                              hop_length=hop_length,
                                              aggregate=np.median)

    # Do we have any onsets to grab?
    if not onset_envelope.any():
        return (0, np.array([], dtype=int))

    # Estimate BPM if one was not provided
    if bpm is None:
        bpm = tempo(onset_envelope=onset_envelope,
                    sr=sr,
                    hop_length=hop_length,
                    start_bpm=start_bpm)[0]

    # Then, run the tracker
    beats = __beat_tracker(onset_envelope,
                           bpm,
                           float(sr) / hop_length,
                           tightness,
                           trim)

    if units == 'frames':
        pass
    elif units == 'samples':
        beats = core.frames_to_samples(beats, hop_length=hop_length)
    elif units == 'time':
        beats = core.frames_to_time(beats, hop_length=hop_length, sr=sr)
    else:
        raise ParameterError('Invalid unit type: {}'.format(units))

    return (bpm, beats)
def beat_track(y=None, sr=22050, onset_envelope=None, hop_length=512,
               start_bpm=120.0, tightness=100, trim=True, bpm=None,
               units='frames'):
    r'''Dynamic programming beat tracker.

    Beats are detected in three stages, following the method of [1]_:
      1. Measure onset strength
      2. Estimate tempo from onset correlation
      3. Pick peaks in onset strength approximately consistent with estimated
         tempo

    .. [1] 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=(n,)] 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]
        (optional) If provided, use `bpm` as the tempo instead of
        estimating it from `onsets`.

    units : {'frames', 'samples', 'time'}
        The units to encode detected beat events in.
        By default, 'frames' are used.


    Returns
    -------

    tempo : float [scalar, non-negative]
        estimated global tempo (in beats per minute)

    beats : np.ndarray [shape=(m,)]
        estimated beat event locations in the specified units
        (default is frame indices)

    .. 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.util.example_audio_file())

    >>> tempo, beats = librosa.beat.beat_track(y=y, sr=sr)
    >>> tempo
    64.599609375


    Print the first 20 beat frames

    >>> beats[:20]
    array([ 320,  357,  397,  436,  480,  525,  569,  609,  658,
            698,  737,  777,  817,  857,  896,  936,  976, 1016,
           1055, 1095])


    Or print them as timestamps

    >>> librosa.frames_to_time(beats[:20], sr=sr)
    array([  7.43 ,   8.29 ,   9.218,  10.124,  11.146,  12.19 ,
            13.212,  14.141,  15.279,  16.208,  17.113,  18.042,
            18.971,  19.9  ,  20.805,  21.734,  22.663,  23.591,
            24.497,  25.426])


    Track beats using a pre-computed onset envelope

    >>> onset_env = librosa.onset.onset_strength(y, sr=sr,
    ...                                          aggregate=np.median)
    >>> tempo, beats = librosa.beat.beat_track(onset_envelope=onset_env,
    ...                                        sr=sr)
    >>> tempo
    64.599609375
    >>> beats[:20]
    array([ 320,  357,  397,  436,  480,  525,  569,  609,  658,
            698,  737,  777,  817,  857,  896,  936,  976, 1016,
           1055, 1095])


    Plot the beat events against the onset strength envelope

    >>> import matplotlib.pyplot as plt
    >>> hop_length = 512
    >>> plt.figure(figsize=(8, 4))
    >>> times = librosa.frames_to_time(np.arange(len(onset_env)),
    ...                                sr=sr, hop_length=hop_length)
    >>> plt.plot(times, librosa.util.normalize(onset_env),
    ...          label='Onset strength')
    >>> plt.vlines(times[beats], 0, 1, alpha=0.5, color='r',
    ...            linestyle='--', label='Beats')
    >>> plt.legend(frameon=True, framealpha=0.75)
    >>> # Limit the plot to a 15-second window
    >>> plt.xlim(15, 30)
    >>> plt.gca().xaxis.set_major_formatter(librosa.display.TimeFormatter())
    >>> plt.tight_layout()
    '''

    # First, get the frame->beat strength profile if we don't already have one
    if onset_envelope is None:
        if y is None:
            raise ParameterError('y or onset_envelope must be provided')

        onset_envelope = onset.onset_strength(y=y,
                                              sr=sr,
                                              hop_length=hop_length,
                                              aggregate=np.median)

    # Do we have any onsets to grab?
    if not onset_envelope.any():
        return (0, np.array([], dtype=int))

    # Estimate BPM if one was not provided
    if bpm is None:
        bpm = tempo(onset_envelope=onset_envelope,
                    sr=sr,
                    hop_length=hop_length,
                    start_bpm=start_bpm)[0]

    # Then, run the tracker
    beats = __beat_tracker(onset_envelope,
                           bpm,
                           float(sr) / hop_length,
                           tightness,
                           trim)

    if units == 'frames':
        pass
    elif units == 'samples':
        beats = core.frames_to_samples(beats, hop_length=hop_length)
    elif units == 'time':
        beats = core.frames_to_time(beats, hop_length=hop_length, sr=sr)
    else:
        raise ParameterError('Invalid unit type: {}'.format(units))

    return (bpm, beats)
def beat_track ( y = None , sr = 22050 , onset_envelope = None , hop_length = 512 , start_bpm = 120.0 , tightness = 100 , trim = True , bpm = None , units = 'frames' ) : # First, get the frame->beat strength profile if we don't already have one if onset_envelope is None : if y is None : raise ParameterError ( 'y or onset_envelope must be provided' ) onset_envelope = onset . onset_strength ( y = y , sr = sr , hop_length = hop_length , aggregate = np . median ) # Do we have any onsets to grab? if not onset_envelope . any ( ) : return ( 0 , np . array ( [ ] , dtype = int ) ) # Estimate BPM if one was not provided if bpm is None : bpm = tempo ( onset_envelope = onset_envelope , sr = sr , hop_length = hop_length , start_bpm = start_bpm ) [ 0 ] # Then, run the tracker beats = __beat_tracker ( onset_envelope , bpm , float ( sr ) / hop_length , tightness , trim ) if units == 'frames' : pass elif units == 'samples' : beats = core . frames_to_samples ( beats , hop_length = hop_length ) elif units == 'time' : beats = core . frames_to_time ( beats , hop_length = hop_length , sr = sr ) else : raise ParameterError ( 'Invalid unit type: {}' . format ( units ) ) return ( bpm , beats )
r'''Dynamic programming beat tracker.

    Beats are detected in three stages, following the method of [1]_:
      1. Measure onset strength
      2. Estimate tempo from onset correlation
      3. Pick peaks in onset strength approximately consistent with estimated
         tempo

    .. [1] 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=(n,)] 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]
        (optional) If provided, use `bpm` as the tempo instead of
        estimating it from `onsets`.

    units : {'frames', 'samples', 'time'}
        The units to encode detected beat events in.
        By default, 'frames' are used.


    Returns
    -------

    tempo : float [scalar, non-negative]
        estimated global tempo (in beats per minute)

    beats : np.ndarray [shape=(m,)]
        estimated beat event locations in the specified units
        (default is frame indices)

    .. 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.util.example_audio_file())

    >>> tempo, beats = librosa.beat.beat_track(y=y, sr=sr)
    >>> tempo
    64.599609375


    Print the first 20 beat frames

    >>> beats[:20]
    array([ 320,  357,  397,  436,  480,  525,  569,  609,  658,
            698,  737,  777,  817,  857,  896,  936,  976, 1016,
           1055, 1095])


    Or print them as timestamps

    >>> librosa.frames_to_time(beats[:20], sr=sr)
    array([  7.43 ,   8.29 ,   9.218,  10.124,  11.146,  12.19 ,
            13.212,  14.141,  15.279,  16.208,  17.113,  18.042,
            18.971,  19.9  ,  20.805,  21.734,  22.663,  23.591,
            24.497,  25.426])


    Track beats using a pre-computed onset envelope

    >>> onset_env = librosa.onset.onset_strength(y, sr=sr,
    ...                                          aggregate=np.median)
    >>> tempo, beats = librosa.beat.beat_track(onset_envelope=onset_env,
    ...                                        sr=sr)
    >>> tempo
    64.599609375
    >>> beats[:20]
    array([ 320,  357,  397,  436,  480,  525,  569,  609,  658,
            698,  737,  777,  817,  857,  896,  936,  976, 1016,
           1055, 1095])


    Plot the beat events against the onset strength envelope

    >>> import matplotlib.pyplot as plt
    >>> hop_length = 512
    >>> plt.figure(figsize=(8, 4))
    >>> times = librosa.frames_to_time(np.arange(len(onset_env)),
    ...                                sr=sr, hop_length=hop_length)
    >>> plt.plot(times, librosa.util.normalize(onset_env),
    ...          label='Onset strength')
    >>> plt.vlines(times[beats], 0, 1, alpha=0.5, color='r',
    ...            linestyle='--', label='Beats')
    >>> plt.legend(frameon=True, framealpha=0.75)
    >>> # Limit the plot to a 15-second window
    >>> plt.xlim(15, 30)
    >>> plt.gca().xaxis.set_major_formatter(librosa.display.TimeFormatter())
    >>> plt.tight_layout()
r Dynamic programming beat tracker .