librosa/core/time_frequency.py
def mel_to_hz(mels, htk=False):
    """Convert mel bin numbers to frequencies

    Examples
    --------
    >>> librosa.mel_to_hz(3)
    200.

    >>> librosa.mel_to_hz([1,2,3,4,5])
    array([  66.667,  133.333,  200.   ,  266.667,  333.333])

    Parameters
    ----------
    mels          : np.ndarray [shape=(n,)], float
        mel bins to convert
    htk           : bool
        use HTK formula instead of Slaney

    Returns
    -------
    frequencies   : np.ndarray [shape=(n,)]
        input mels in Hz

    See Also
    --------
    hz_to_mel
    """

    mels = np.asanyarray(mels)

    if htk:
        return 700.0 * (10.0**(mels / 2595.0) - 1.0)

    # Fill in the linear scale
    f_min = 0.0
    f_sp = 200.0 / 3
    freqs = f_min + f_sp * mels

    # And now the nonlinear scale
    min_log_hz = 1000.0                         # beginning of log region (Hz)
    min_log_mel = (min_log_hz - f_min) / f_sp   # same (Mels)
    logstep = np.log(6.4) / 27.0                # step size for log region

    if mels.ndim:
        # If we have vector data, vectorize
        log_t = (mels >= min_log_mel)
        freqs[log_t] = min_log_hz * np.exp(logstep * (mels[log_t] - min_log_mel))
    elif mels >= min_log_mel:
        # If we have scalar data, check directly
        freqs = min_log_hz * np.exp(logstep * (mels - min_log_mel))

    return freqs
def mel_to_hz(mels, htk=False):
    mels = np.asanyarray(mels)

    if htk:
        return 700.0 * (10.0**(mels / 2595.0) - 1.0)

    # Fill in the linear scale
    f_min = 0.0
    f_sp = 200.0 / 3
    freqs = f_min + f_sp * mels

    # And now the nonlinear scale
    min_log_hz = 1000.0                         # beginning of log region (Hz)
    min_log_mel = (min_log_hz - f_min) / f_sp   # same (Mels)
    logstep = np.log(6.4) / 27.0                # step size for log region

    if mels.ndim:
        # If we have vector data, vectorize
        log_t = (mels >= min_log_mel)
        freqs[log_t] = min_log_hz * np.exp(logstep * (mels[log_t] - min_log_mel))
    elif mels >= min_log_mel:
        # If we have scalar data, check directly
        freqs = min_log_hz * np.exp(logstep * (mels - min_log_mel))

    return freqs
def mel_to_hz ( mels , htk = False ) : mels = np . asanyarray ( mels ) if htk : return 700.0 * ( 10.0 ** ( mels / 2595.0 ) - 1.0 ) # Fill in the linear scale f_min = 0.0 f_sp = 200.0 / 3 freqs = f_min + f_sp * mels # And now the nonlinear scale min_log_hz = 1000.0 # beginning of log region (Hz) min_log_mel = ( min_log_hz - f_min ) / f_sp # same (Mels) logstep = np . log ( 6.4 ) / 27.0 # step size for log region if mels . ndim : # If we have vector data, vectorize log_t = ( mels >= min_log_mel ) freqs [ log_t ] = min_log_hz * np . exp ( logstep * ( mels [ log_t ] - min_log_mel ) ) elif mels >= min_log_mel : # If we have scalar data, check directly freqs = min_log_hz * np . exp ( logstep * ( mels - min_log_mel ) ) return freqs
Convert mel bin numbers to frequencies

    Examples
    --------
    >>> librosa.mel_to_hz(3)
    200.

    >>> librosa.mel_to_hz([1,2,3,4,5])
    array([  66.667,  133.333,  200.   ,  266.667,  333.333])

    Parameters
    ----------
    mels          : np.ndarray [shape=(n,)], float
        mel bins to convert
    htk           : bool
        use HTK formula instead of Slaney

    Returns
    -------
    frequencies   : np.ndarray [shape=(n,)]
        input mels in Hz

    See Also
    --------
    hz_to_mel
Convert mel bin numbers to frequencies