Plot feature map clusters

This example shows how to cluster rhythmic patterns from a feature map.

This is based on the rhythmic patterns analysis proposed in [CIM2014].

# Code source: Martín Rocamora
# License: MIT

Imports

• matplotlib for visualization

from __future__ import print_function
import matplotlib.pyplot as plt
import carat

We group rhythmic patterns into clusters to aid the analysis of their differences and similarities.

First, we’ll load one of the audio files included in carat.

audio_path = carat.util.example_audio_file(num_file=1)

y, sr = carat.audio.load(audio_path)

Next, we’ll load the annotations provided for the example audio file.

annotations_path = carat.util.example_beats_file(num_file=1)

beats, beat_labs = carat.annotations.load_beats(annotations_path)
downbeats, downbeat_labs = carat.annotations.load_downbeats(annotations_path)

Then, we’ll compute the accentuation feature.

Note: This example is tailored towards the rhythmic patterns of the lowest sounding of the three drum types taking part in the recording, so the analysis focuses on the low frequencies (20 to 200 Hz).

acce, times, _ = carat.features.accentuation_feature(y, sr, minfreq=20, maxfreq=200)

Next, we’ll compute the feature map.

n_beats = int(round(beats.size/downbeats.size))
n_tatums = 4

map_acce, _, _, _ = carat.features.feature_map(acce, times, beats, downbeats, n_beats=n_beats,
                                               n_tatums=n_tatums)

Then, we’ll group rhythmic patterns into clusters. This is done using the classical K-means method with Euclidean distance (but other clustering methods and distance measures can be used too).

Note: The number of clusters n_clusters has to be specified as an input parameter.

n_clusters = 4

cluster_labs, centroids, _ = carat.clustering.rhythmic_patterns(map_acce, n_clusters=n_clusters)

Finally we plot the feature map and the obtained clusters.

plt.figure(figsize=(12, 6))
# plot feature map
ax1 = plt.subplot(211)
carat.display.map_show(map_acce, ax=ax1, n_tatums=n_tatums)
# plot feature map with clusters in colors
ax2 = plt.subplot(212)
carat.display.map_show(map_acce, ax=ax2, n_tatums=n_tatums, clusters=cluster_labs)

plt.show()