Aggregate features and ADABOOST for music classification

Abstract

We present an algorithm that predicts musical genre and artist from an audio waveform. Our method uses the ensemble learner ADABOOST to select from a set of audio features that have been extracted from segmented audio and then aggregated. Our classifier proved to be the most effective method for genre classification at the recent MIREX 2005 international contests in music information extraction, and the second-best method for recognizing artists. This paper describes our method in detail, from feature extraction to song classification, and presents an evaluation of our method on three genre databases and two artist-recognition databases. Furthermore, we present evidence collected from a variety of popular features and classifiers that the technique of classifying features aggregated over segments of audio is better than classifying either entire songs or individual short-timescale features.

Social Tagging and Music Information Retrieval

“…The average genre overlap across these 11 genres, weighted by the class size is 17.32% This overlap suggests an upper bound for automatic genre classification accuracy. Current state-of-the-art automatic genre classification accuracy of 82.34% is approaching this upper bound [5]. This fuzziness is perhaps the root cause of the increasingly smaller performance improvements in automatic genre classification [21].…”

Introduction to multivariate discrimination

“…The output y of the system is binary, that is, y ∈ C = {music, speech}. In [44] we stay within the music classification domain but we tackle a more difficult problem: finding the performing artist and the genre of a song based on the audio signal of the first [30]s of the song. The first module of the classifier pipeline is an elaborate signal processor that collects a vector of features for each [2]s segment and then aggregates them to constitute an observation vector x with about 800 components per song.…”

Automatic music genre classification based on musical instrument track separation

“…Automatic music genre classification (AMGC) has been exploited quite thoroughly in recent years by the research community (ISMIR conferences, ISMIR (2016)) and is one of the most popular search query choices within the MIR domain (Bergstra et al 2006;Burred 2014;Kostek 2005;Ntalampiras 2013;Schedl et al 2014;Silla et al 2007;Sturm 2013;Tzanetakis et al 2002)). On a smaller scale, a survey, focusing on AMGC was presented by Silla et al (2007).…”

“…They used a combination of binary classifiers, the results of which were merged to produce the final music genre labeling (Silla et al 2007). Another, non-conventional approach was shown in the work by Sturm (2014), as well as by Bergstra et al (2006). The AdaBoost algorithm, performing the classification iteratively by combining the weighted votes of several weak learners, was utilized.…”

“…1,000-2,000 music pieces assigned to a few popular music genres (see e.g. Bergstra et al (2006)). There are some larger collections of music excerpts, e.g.…”