320cbfd379a8e8de6ff49eeba35ca16750e80d38,librosa/core/audio.py,,autocorrelate,#,357

Before Change

    

    result = scipy.signal.fftconvolve(y, y[::-1], mode="full")

    result = result[int(len(result)/2):]

    if max_size is None:
        return result
    else:
        max_size = int(max_size)

    return result[:max_size]

After Change

    // Compute the power spectrum along the chosen axis
    // Pad out the signal to support full-length auto-correlation.
    powspec = np.abs(fft.fft(y, n=2 * y.shape[axis] + 1, axis=axis))**2

    // Convert back to time domain
    autocorr = fft.ifft(powspec, axis=axis, overwrite_x=True)

    // Slice down to max_size
    subslice = [Ellipsis] * autocorr.ndim
    subslice[axis] = slice(max_size)

    return autocorr[subslice]


@cache
def zero_crossings(y, threshold=1e-10, ref_magnitude=None, pad=True,

Instances