librosa/core/time_frequency.py
def hz_to_mel(frequencies, htk=False):
    """Convert Hz to Mels

    Examples
    --------
    >>> librosa.hz_to_mel(60)
    0.9
    >>> librosa.hz_to_mel([110, 220, 440])
    array([ 1.65,  3.3 ,  6.6 ])

    Parameters
    ----------
    frequencies   : number or np.ndarray [shape=(n,)] , float
        scalar or array of frequencies
    htk           : bool
        use HTK formula instead of Slaney

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

    See Also
    --------
    mel_to_hz
    """

    frequencies = np.asanyarray(frequencies)

    if htk:
        return 2595.0 * np.log10(1.0 + frequencies / 700.0)

    # Fill in the linear part
    f_min = 0.0
    f_sp = 200.0 / 3

    mels = (frequencies - f_min) / f_sp

    # Fill in the log-scale part

    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 frequencies.ndim:
        # If we have array data, vectorize
        log_t = (frequencies >= min_log_hz)
        mels[log_t] = min_log_mel + np.log(frequencies[log_t]/min_log_hz) / logstep
    elif frequencies >= min_log_hz:
        # If we have scalar data, heck directly
        mels = min_log_mel + np.log(frequencies / min_log_hz) / logstep

    return mels
def hz_to_mel(frequencies, htk=False):
    frequencies = np.asanyarray(frequencies)

    if htk:
        return 2595.0 * np.log10(1.0 + frequencies / 700.0)

    # Fill in the linear part
    f_min = 0.0
    f_sp = 200.0 / 3

    mels = (frequencies - f_min) / f_sp

    # Fill in the log-scale part

    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 frequencies.ndim:
        # If we have array data, vectorize
        log_t = (frequencies >= min_log_hz)
        mels[log_t] = min_log_mel + np.log(frequencies[log_t]/min_log_hz) / logstep
    elif frequencies >= min_log_hz:
        # If we have scalar data, heck directly
        mels = min_log_mel + np.log(frequencies / min_log_hz) / logstep

    return mels
def hz_to_mel ( frequencies , htk = False ) : frequencies = np . asanyarray ( frequencies ) if htk : return 2595.0 * np . log10 ( 1.0 + frequencies / 700.0 ) # Fill in the linear part f_min = 0.0 f_sp = 200.0 / 3 mels = ( frequencies - f_min ) / f_sp # Fill in the log-scale part 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 frequencies . ndim : # If we have array data, vectorize log_t = ( frequencies >= min_log_hz ) mels [ log_t ] = min_log_mel + np . log ( frequencies [ log_t ] / min_log_hz ) / logstep elif frequencies >= min_log_hz : # If we have scalar data, heck directly mels = min_log_mel + np . log ( frequencies / min_log_hz ) / logstep return mels
Convert Hz to Mels

    Examples
    --------
    >>> librosa.hz_to_mel(60)
    0.9
    >>> librosa.hz_to_mel([110, 220, 440])
    array([ 1.65,  3.3 ,  6.6 ])

    Parameters
    ----------
    frequencies   : number or np.ndarray [shape=(n,)] , float
        scalar or array of frequencies
    htk           : bool
        use HTK formula instead of Slaney

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

    See Also
    --------
    mel_to_hz
Convert Hz to Mels