def METHOD_NAME(beat_times, song):
    '''Load a ground-truth segmentation, and align times to the nearest
    detected beats.
    Arguments:
        beat_times -- array
        song -- path to the audio file
    Returns:
        segment_beats -- array
            beat-aligned segment boundaries
        segment_times -- array
            true segment times
        segment_labels -- array
            list of segment labels
    '''
    try:
        segment_times, segment_labels = msaf.io.read_references(song)
    except:
        return None, None, None
    segment_times = np.asarray(segment_times)
    # Map to intervals
    segment_intervals = msaf.utils.times_to_intervals(segment_times)
    # Map beats to intervals
    beat_intervals = np.asarray(zip(beat_times[:-1], beat_times[1:]))
    # Map beats to segments
    beat_segment_ids = librosa.util.match_intervals(beat_intervals,
                                                    segment_intervals)
    segment_beats = []
    segment_times_out = []
    segment_labels_out = []
    # print segment_times, beat_segment_ids, len(beat_times),
    # len(beat_segment_ids)
    for i in range(segment_times.shape[0]):
        hits = np.argwhere(beat_segment_ids == i)
        if len(hits) > 0 and i < len(segment_intervals) and \
                i < len(segment_labels):
            segment_beats.extend(hits[0])
            segment_times_out.append(segment_intervals[i, :])
            segment_labels_out.append(segment_labels[i])
    # Pull out the segment start times
    segment_beats = list(segment_beats)
    # segment_times_out = np.asarray(
    #  segment_times_out)[:, 0].squeeze().reshape((-1, 1))
    # if segment_times_out.ndim == 0:
    #    segment_times_out = segment_times_out[np.newaxis]
    segment_times_out = segment_times
    return segment_beats, segment_times_out, segment_labels_out