audio_tools.py 文件源码

def nsgitf_real(c, c_dc, c_nyq, multiscale, shift):
    """
    Nonstationary Inverse Gabor Transform on real valued signal

    References
    ----------
    Velasco G. A., Holighaus N., Dorfler M., Grill T.
    Constructing an invertible constant-Q transform with nonstationary Gabor
    frames, Proceedings of the 14th International Conference on Digital
    Audio Effects (DAFx 11), Paris, France, 2011

    Holighaus N., Dorfler M., Velasco G. A. and Grill T.
    A framework for invertible, real-time constant-Q transforms, submitted.

    Original matlab code copyright follows:

    AUTHOR(s) : Monika Dorfler, Gino Angelo Velasco, Nicki Holighaus, 2010-2011

    COPYRIGHT : (c) NUHAG, Dept.Math., University of Vienna, AUSTRIA
    http://nuhag.eu/
    Permission is granted to modify and re-distribute this
    code in any manner as long as this notice is preserved.
    All standard disclaimers apply.
    """
    c_l = []
    c_l.append(c_dc)
    c_l.extend([ci for ci in c])
    c_l.append(c_nyq)

    posit = np.cumsum(shift)
    seq_len = posit[-1]
    posit -= shift[0]
    out = np.zeros((seq_len,)).astype(c_l[1].dtype)

    for ii in range(len(c_l)):
        filt_len = len(multiscale[ii])
        win_range = posit[ii] + np.arange(-np.floor(filt_len / 2),
                                          np.ceil(filt_len / 2))
        win_range = (win_range % seq_len).astype(np.int)
        temp = np.fft.fft(c_l[ii]) * len(c_l[ii])

        fs_new_bins = len(c_l[ii])
        fk_bins = posit[ii]
        displace = int(fk_bins - np.floor(fk_bins / fs_new_bins) * fs_new_bins)
        temp = np.roll(temp, -displace)
        l = np.arange(len(temp) - np.floor(filt_len / 2), len(temp))
        r = np.arange(np.ceil(filt_len / 2))
        temp_idx = (np.concatenate((l, r)) % len(temp)).astype(np.int)
        temp = temp[temp_idx]
        lf = np.arange(filt_len - np.floor(filt_len / 2), filt_len)
        rf = np.arange(np.ceil(filt_len / 2))
        filt_idx = np.concatenate((lf, rf)).astype(np.int)
        m = multiscale[ii][filt_idx]
        out[win_range] = out[win_range] + m * temp

    nyq_bin = np.floor(seq_len / 2) + 1
    out_idx = np.arange(
        nyq_bin - np.abs(1 - seq_len % 2) - 1, 0, -1).astype(np.int)
    out[nyq_bin:] = np.conj(out[out_idx])
    t_out = np.real(np.fft.ifft(out)).astype(np.float64)
    return t_out