comparison cnmf.py @ 0:26838b1f560f

initial commit of a segmenter project

+"""
+C-NMF method for segmentation, modified from here:
+
+Nieto, O., Jehan, T., Convex Non-negative Matrix Factorization For Automatic
+Music Structure Identification. Proc. of the 38th IEEE International Conference
+on Acoustics, Speech, and Signal Processing (ICASSP). Vancouver, Canada, 2013.
+"""
+
+__author__ = "Oriol Nieto"
+__copyright__ = "Copyright 2014, Music and Audio Research Lab (MARL)"
+__license__ = "GPL"
+__version__ = "1.0"
+__email__ = "[e-mail]"
+
+import numpy as np
+import pymf
+
+# Local stuff
+from utils import SegUtil
+
+
+def cnmf(S, rank, niter=500):
+    """(Convex) Non-Negative Matrix Factorization.
+
+    Parameters
+    ----------
+    S: np.array(p, N)
+        Features matrix. p row features and N column observations.
+    rank: int
+        Rank of decomposition
+    niter: int
+        Number of iterations to be used
+
+    Returns
+    -------
+    F: np.array
+        Cluster matrix (decomposed matrix)
+    G: np.array
+        Activation matrix (decomposed matrix)
+        (s.t. S ~= F * G)
+    """
+    nmf_mdl = pymf.CNMF(S, num_bases=rank)
+    nmf_mdl.factorize(niter=niter)
+    F = np.asarray(nmf_mdl.W)
+    G = np.asarray(nmf_mdl.H)
+    return F, G
+
+
+def most_frequent(x):
+    """Returns the most frequent value in x."""
+    return np.argmax(np.bincount(x))
+
+
+def compute_labels(X, rank, R, bound_idxs, niter=300):
+    """Computes the labels using the bounds."""
+
+    X = X.T
+    try:
+        F, G = cnmf(X, rank, niter=niter)
+    except:
+        return [1]
+
+    label_frames = filter_activation_matrix(G.T, R)
+    label_frames = np.asarray(label_frames, dtype=int)
+
+    # Get labels from the label frames
+    labels = []
+    bound_inters = zip(bound_idxs[:-1], bound_idxs[1:])
+    for bound_inter in bound_inters:
+        if bound_inter[1] - bound_inter[0] <= 0:
+            labels.append(np.max(label_frames) + 1)
+        else:
+            labels.append(most_frequent(
+                label_frames[bound_inter[0]:bound_inter[1]]))
+
+    return labels
+
+
+def filter_activation_matrix(G, R):
+    """Filters the activation matrix G, and returns a flattened copy."""
+    idx = np.argmax(G, axis=1)
+    max_idx = np.arange(G.shape[0])
+    max_idx = (max_idx, idx.flatten())
+    G[:, :] = 0
+    G[max_idx] = idx + 1
+    G = np.sum(G, axis=1)
+    G = utils.median_filter(G[:, np.newaxis], R)
+    return G.flatten()
+
+
+def segmentation(X, rank, R, h, niter=300):
+    """
+    Gets the segmentation (boundaries and labels) from the factorization
+    matrices.
+
+    Parameters
+    ----------
+    X: np.array()
+        Features matrix (e.g. chromagram)
+    rank: int
+        Rank of decomposition
+    R: int
+        Size of the median filter for activation matrix
+    niter: int
+        Number of iterations for k-means
+    bound_idxs : list
+        Use previously found boundaries (None to detect them)
+
+    Returns
+    -------
+    bounds_idx: np.array
+        Bound indeces found
+    labels: np.array
+        Indeces of the labels representing the similarity between segments.
+    """
+
+    # Filter
+    X = utils.median_filter(X, M=h)
+    X = X.T
+
+    # Find non filtered boundaries
+    bound_idxs = None
+    while True:
+        if bound_idxs is None:
+            try:
+                F, G = cnmf(X, rank, niter=niter)
+            except:
+                return np.empty(0), [1]
+
+            # Filter G
+            G = filter_activation_matrix(G.T, R)
+            if bound_idxs is None:
+                bound_idxs = np.where(np.diff(G) != 0)[0] + 1
+
+        if len(np.unique(bound_idxs)) <= 2:
+            rank += 1
+            bound_idxs = None
+        else:
+            break
+
+    return bound_idxs