48a579ae1638f5c60e62d8f5c3bad35e0dcef249,librosa/beat.py,,onset_estimate_bpm,#,145

Before Change

    //   this fails if ac_window > length of song   

    bpms                    = 60.0 * fft_resolution / (numpy.arange(1, ac_window+1))
    x_corr_weighting        = numpy.exp(-0.5 * ((numpy.log2(bpms) - numpy.log2(start_bpm)) / bpm_std)**2)

    // Compute the weighted autocorrelation
    x_corr                  = x_corr * x_corr_weighting

    // Get the local maximum of weighted correlation
    x_peaks                 = librosa.localmax(x_corr)

After Change

    bpms    = 60.0 * fft_res / (numpy.arange(1, ac_window+1))

    // Smooth the autocorrelation by a log-normal distribution
    x_corr  = x_corr * numpy.exp(-0.5 * ((numpy.log2(bpms / start_bpm)) / BPM_STD)**2)

    // Get the local maximum of weighted correlation
    x_peaks = librosa.localmax(x_corr)

    // Zero out all peaks before the first negative
    x_peaks[:numpy.argmax(x_corr < 0)] = False

    // Find the largest (local) max
    start_period    = numpy.argmax(x_peaks * x_corr)

    // Choose the best peak out of .33, .5, 2, 3 * start_period
    candidates      = numpy.multiply(start_period, [1.0/3, 1.0/2, 1.0, 2.0, 3.0])
    candidates      = candidates.astype(int)
    candidates      = candidates[candidates < ac_window]

    best_period     = numpy.argmax(x_corr[candidates])

Instances