BINS_PER_OCTAVE = 12 * 3

N_OCTAVES = 7

C = librosa.amplitude_to_db(np.abs(librosa.cqt(y=y, sr=sr,

bins_per_octave=BINS_PER_OCTAVE,

n_bins=N_OCTAVES * BINS_PER_OCTAVE)),

ref=np.max)

# these will already be fixed if librosa, but doesn't matter if we redo.

beat_times = [x['start'] for x in beats_in_seconds]

beat_times = np.array(beat_times)

# Get frames of beats, clipped to zero frame.

beats = librosa.util.fix_frames(librosa.time_to_frames(beat_times, sr=sr), x_min=0)

# Now get the fixed beat_times (basically 0.0 again)

beat_times = librosa.frames_to_time(beats, sr=sr) #

Csync = librosa.util.sync(C, beats, aggregate=np.median)

R = librosa.segment.recurrence_matrix(Csync, width=3, mode='affinity',sym=True)

df = librosa.segment.timelag_filter(scipy.ndimage.median_filter)

Rf = df(R, size=(1, 7))

mfcc = librosa.feature.mfcc(y=y, sr=sr)

Msync = librosa.util.sync(mfcc, beats)

path_distance = np.sum(np.diff(Msync, axis=1)**2, axis=0)

sigma = np.median(path_distance)

path_sim = np.exp(-path_distance / sigma)

R_path = np.diag(path_sim, k=1) + np.diag(path_sim, k=-1)

deg_path = np.sum(R_path, axis=1)

deg_rec = np.sum(Rf, axis=1)

mu = deg_path.dot(deg_path + deg_rec) / np.sum((deg_path + deg_rec)**2)

A = mu * Rf + (1 - mu) * R_path

L = scipy.sparse.csgraph.laplacian(A, normed=True)

evals, evecs = scipy.linalg.eigh(L)

evecs = scipy.ndimage.median_filter(evecs, size=(9, 1))

Cnorm = np.cumsum(evecs**2, axis=1)**0.5

k = dimensions

X = evecs[:, :k] / Cnorm[:, k-1:k]

num_clusters = clusters

KM = sklearn.cluster.KMeans(n_clusters=num_clusters, n_init=100)

seg_ids = KM.fit_predict(X)

bound_beats = 1 + np.flatnonzero(seg_ids[:-1] != seg_ids[1:])

bound_beats = librosa.util.fix_frames(bound_beats, x_min=0)

bound_segs = list(seg_ids[bound_beats])

bound_frames = beats[bound_beats]

# Make sure we cover to the end of the track

bound_frames = librosa.util.fix_frames(bound_frames,

x_min=None,

x_max=C.shape[1]-1)

bound_times = librosa.frames_to_time(bound_frames, sr=sr)

freqs = librosa.cqt_frequencies(n_bins=C.shape[0],

fmin=librosa.note_to_hz('C1'),

bins_per_octave=BINS_PER_OCTAVE)

# return these values, then we can plot together (and the spectragrams etc)