Onset detection for drum loop classification

Abstract

The main objective of this report is to be able to perform drum beat detection through a semi-automatic way to produce the drum samples and then use these drum samples to produce a general classifier that is able to identify the drum beats in any music track. An algorithm was first introduced to perform onset detection on percussive sounds for a small period of music track of about six seconds. The user is involved in labeling which of these percussive sounds are the bass and snare drum, hence a semi-automatic way approach in drum beat detection. A small database of 21 music tracks was used in this project for performance evaluation. Performance evaluation of the onset detection algorithm is done by evaluating the average precision rate and recall rate of the algorithm for all the music tracks in the database. The percussive onset detection algorithm had an average precision rate of 0.714 and average recall rate of 0.953.

After the onset detection, the next task was to train a classifier using the previous drum samples detected previously by the algorithm. There were two different scenarios in achieving this task. The first scenario was to classify the target vector into 2 classes with bass and snare drum, and the second scenario was to classify the target vector into 3 classes with the bass drum, snare drum and other onsets which are neither bass nor snare. The results of these two scenarios and possibilities for future work to solve this issue will be discussed in this report.