librosa/core/constantq.py
def pseudo_cqt(y, sr=22050, hop_length=512, fmin=None, n_bins=84,
               bins_per_octave=12, tuning=0.0, filter_scale=1,
               norm=1, sparsity=0.01, window='hann', scale=True,
               pad_mode='reflect'):
    '''Compute the pseudo constant-Q transform of an audio signal.

    This uses a single fft size that is the smallest power of 2 that is greater
    than or equal to the max of:

        1. The longest CQT filter
        2. 2x the hop_length

    Parameters
    ----------
    y : np.ndarray [shape=(n,)]
        audio time series

    sr : number > 0 [scalar]
        sampling rate of `y`

    hop_length : int > 0 [scalar]
        number of samples between successive CQT columns.

    fmin : float > 0 [scalar]
        Minimum frequency. Defaults to C1 ~= 32.70 Hz

    n_bins : int > 0 [scalar]
        Number of frequency bins, starting at `fmin`

    bins_per_octave : int > 0 [scalar]
        Number of bins per octave

    tuning : None or float in `[-0.5, 0.5)`
        Tuning offset in fractions of a bin (cents).

        If `None`, tuning will be automatically estimated from the signal.

    filter_scale : float > 0
        Filter filter_scale factor. Larger values use longer windows.

    sparsity : float in [0, 1)
        Sparsify the CQT basis by discarding up to `sparsity`
        fraction of the energy in each basis.

        Set `sparsity=0` to disable sparsification.

    window : str, tuple, number, or function
        Window specification for the basis filters.
        See `filters.get_window` for details.

    pad_mode : string
        Padding mode for centered frame analysis.

        See also: `librosa.core.stft` and `np.pad`.

    Returns
    -------
    CQT : np.ndarray [shape=(n_bins, t), dtype=np.float]
        Pseudo Constant-Q energy for each frequency at each time.

    Raises
    ------
    ParameterError
        If `hop_length` is not an integer multiple of
        `2**(n_bins / bins_per_octave)`

        Or if `y` is too short to support the frequency range of the CQT.

    Notes
    -----
    This function caches at level 20.

    '''

    if fmin is None:
        # C1 by default
        fmin = note_to_hz('C1')

    if tuning is None:
        tuning = estimate_tuning(y=y, sr=sr)

    fft_basis, n_fft, _ = __cqt_filter_fft(sr, fmin, n_bins,
                                           bins_per_octave,
                                           tuning, filter_scale,
                                           norm, sparsity,
                                           hop_length=hop_length,
                                           window=window)

    fft_basis = np.abs(fft_basis)

    # Compute the magnitude STFT with Hann window
    D = np.abs(stft(y, n_fft=n_fft, hop_length=hop_length, pad_mode=pad_mode))

    # Project onto the pseudo-cqt basis
    C = fft_basis.dot(D)

    if scale:
        C /= np.sqrt(n_fft)
    else:
        lengths = filters.constant_q_lengths(sr, fmin,
                                             n_bins=n_bins,
                                             bins_per_octave=bins_per_octave,
                                             tuning=tuning,
                                             window=window,
                                             filter_scale=filter_scale)

        C *= np.sqrt(lengths[:, np.newaxis] / n_fft)

    return C
def pseudo_cqt(y, sr=22050, hop_length=512, fmin=None, n_bins=84,
               bins_per_octave=12, tuning=0.0, filter_scale=1,
               norm=1, sparsity=0.01, window='hann', scale=True,
               pad_mode='reflect'):
    '''Compute the pseudo constant-Q transform of an audio signal.

    This uses a single fft size that is the smallest power of 2 that is greater
    than or equal to the max of:

        1. The longest CQT filter
        2. 2x the hop_length

    Parameters
    ----------
    y : np.ndarray [shape=(n,)]
        audio time series

    sr : number > 0 [scalar]
        sampling rate of `y`

    hop_length : int > 0 [scalar]
        number of samples between successive CQT columns.

    fmin : float > 0 [scalar]
        Minimum frequency. Defaults to C1 ~= 32.70 Hz

    n_bins : int > 0 [scalar]
        Number of frequency bins, starting at `fmin`

    bins_per_octave : int > 0 [scalar]
        Number of bins per octave

    tuning : None or float in `[-0.5, 0.5)`
        Tuning offset in fractions of a bin (cents).

        If `None`, tuning will be automatically estimated from the signal.

    filter_scale : float > 0
        Filter filter_scale factor. Larger values use longer windows.

    sparsity : float in [0, 1)
        Sparsify the CQT basis by discarding up to `sparsity`
        fraction of the energy in each basis.

        Set `sparsity=0` to disable sparsification.

    window : str, tuple, number, or function
        Window specification for the basis filters.
        See `filters.get_window` for details.

    pad_mode : string
        Padding mode for centered frame analysis.

        See also: `librosa.core.stft` and `np.pad`.

    Returns
    -------
    CQT : np.ndarray [shape=(n_bins, t), dtype=np.float]
        Pseudo Constant-Q energy for each frequency at each time.

    Raises
    ------
    ParameterError
        If `hop_length` is not an integer multiple of
        `2**(n_bins / bins_per_octave)`

        Or if `y` is too short to support the frequency range of the CQT.

    Notes
    -----
    This function caches at level 20.

    '''

    if fmin is None:
        # C1 by default
        fmin = note_to_hz('C1')

    if tuning is None:
        tuning = estimate_tuning(y=y, sr=sr)

    fft_basis, n_fft, _ = __cqt_filter_fft(sr, fmin, n_bins,
                                           bins_per_octave,
                                           tuning, filter_scale,
                                           norm, sparsity,
                                           hop_length=hop_length,
                                           window=window)

    fft_basis = np.abs(fft_basis)

    # Compute the magnitude STFT with Hann window
    D = np.abs(stft(y, n_fft=n_fft, hop_length=hop_length, pad_mode=pad_mode))

    # Project onto the pseudo-cqt basis
    C = fft_basis.dot(D)

    if scale:
        C /= np.sqrt(n_fft)
    else:
        lengths = filters.constant_q_lengths(sr, fmin,
                                             n_bins=n_bins,
                                             bins_per_octave=bins_per_octave,
                                             tuning=tuning,
                                             window=window,
                                             filter_scale=filter_scale)

        C *= np.sqrt(lengths[:, np.newaxis] / n_fft)

    return C
def pseudo_cqt ( y , sr = 22050 , hop_length = 512 , fmin = None , n_bins = 84 , bins_per_octave = 12 , tuning = 0.0 , filter_scale = 1 , norm = 1 , sparsity = 0.01 , window = 'hann' , scale = True , pad_mode = 'reflect' ) : if fmin is None : # C1 by default fmin = note_to_hz ( 'C1' ) if tuning is None : tuning = estimate_tuning ( y = y , sr = sr ) fft_basis , n_fft , _ = __cqt_filter_fft ( sr , fmin , n_bins , bins_per_octave , tuning , filter_scale , norm , sparsity , hop_length = hop_length , window = window ) fft_basis = np . abs ( fft_basis ) # Compute the magnitude STFT with Hann window D = np . abs ( stft ( y , n_fft = n_fft , hop_length = hop_length , pad_mode = pad_mode ) ) # Project onto the pseudo-cqt basis C = fft_basis . dot ( D ) if scale : C /= np . sqrt ( n_fft ) else : lengths = filters . constant_q_lengths ( sr , fmin , n_bins = n_bins , bins_per_octave = bins_per_octave , tuning = tuning , window = window , filter_scale = filter_scale ) C *= np . sqrt ( lengths [ : , np . newaxis ] / n_fft ) return C
Compute the pseudo constant-Q transform of an audio signal.

    This uses a single fft size that is the smallest power of 2 that is greater
    than or equal to the max of:

        1. The longest CQT filter
        2. 2x the hop_length

    Parameters
    ----------
    y : np.ndarray [shape=(n,)]
        audio time series

    sr : number > 0 [scalar]
        sampling rate of `y`

    hop_length : int > 0 [scalar]
        number of samples between successive CQT columns.

    fmin : float > 0 [scalar]
        Minimum frequency. Defaults to C1 ~= 32.70 Hz

    n_bins : int > 0 [scalar]
        Number of frequency bins, starting at `fmin`

    bins_per_octave : int > 0 [scalar]
        Number of bins per octave

    tuning : None or float in `[-0.5, 0.5)`
        Tuning offset in fractions of a bin (cents).

        If `None`, tuning will be automatically estimated from the signal.

    filter_scale : float > 0
        Filter filter_scale factor. Larger values use longer windows.

    sparsity : float in [0, 1)
        Sparsify the CQT basis by discarding up to `sparsity`
        fraction of the energy in each basis.

        Set `sparsity=0` to disable sparsification.

    window : str, tuple, number, or function
        Window specification for the basis filters.
        See `filters.get_window` for details.

    pad_mode : string
        Padding mode for centered frame analysis.

        See also: `librosa.core.stft` and `np.pad`.

    Returns
    -------
    CQT : np.ndarray [shape=(n_bins, t), dtype=np.float]
        Pseudo Constant-Q energy for each frequency at each time.

    Raises
    ------
    ParameterError
        If `hop_length` is not an integer multiple of
        `2**(n_bins / bins_per_octave)`

        Or if `y` is too short to support the frequency range of the CQT.

    Notes
    -----
    This function caches at level 20.
Compute the pseudo constant - Q transform of an audio signal .