A Comparison of Random Forests and Ferns on Recognition of Instruments in Jazz Recordings

TL;DR

This paper compares the effectiveness of random forests and random ferns in recognizing jazz instruments from raw audio recordings without prior segmentation, demonstrating the potential of these classifiers in music recognition tasks.

Contribution

It introduces the application of random ferns to music recognition and compares their performance with random forests on jazz recordings.

Findings

Random ferns perform comparably to random forests.

No segmentation of audio data is required for classification.

The paper provides experimental results on jazz instrument recognition.

Abstract

In this paper, we first apply random ferns for classification of real music recordings of a jazz band. No initial segmentation of audio data is assumed, i.e., no onset, offset, nor pitch data are needed. The notion of random ferns is described in the paper, to familiarize the reader with this classification algorithm, which was introduced quite recently and applied so far in image recognition tasks. The performance of random ferns is compared with random forests for the same data. The results of experiments are presented in the paper, and conclusions are drawn.