Music-information retrieval in environments containing acoustic noise

Abstract

In the field of Music-Information Retrieval (Music-IR), algorithms are used to analyze musical signals and estimate highlevel features such as tempi and beat locations. These features can then be used in tasks to enhance the experience of listening to music. Most conventional Music-IR algorithms are trained and evaluated on audio that is taken directly from professional recordings with little acoustic noise. However, humans often listen to music in noisy environments, such as dance clubs, crowded bars, and outdoor concert venues. Music-IR algorithms that could function accurately even in these environments would therefore be able to reliably process more of the audio that humans hear. In this paper, I propose methods to perform Music-IR tasks on music that has been contaminated by acoustic noise. These methods incorporate algorithms such as Probabilistic Latent Component Analysis (PLCA) and Harmonic-Percussive Source Separation (HPSS) in order to identify important elements of the noisy musical signal. As an example, a noiserobust beat tracker utilizing these techniques is described.