Predictability of Music Descriptor Time Series and its Application to Cover Song Detection

• Published in: IEEE transactions on audio, speech, and language processing speech, and language processing, 2012-02, Vol.20 (2), p.514-525
• Main Authors: Serra, J., Kantz, H., Serra, X., Andrzejak, R. G.
• Format: Article
• Language: English
• Subjects: Acoustic signal analysis; Anàlisi; Applied sciences; Computational modeling; Exact sciences and technology; information retrieval; Information theory; Information, signal and communications theory; Informàtica; Materials; music; Música; prediction methods; Predictive models; Signal and communications theory; Signal representation. Spectral analysis; Signal, noise; Sèries temporals; Telecommunications and information theory; Timbre; time series; Time series analysis; Tractament per ordinador
• ISSN: 1558-7916; 1558-7924
• DOI: 10.1109/TASL.2011.2162321

Intuitively, music has both predictable and unpredictable components. In this paper, we assess this qualitative statement in a quantitative way using common time series models fitted to state-of-the-art music descriptors. These descriptors cover different musical facets and are extracted from a large collection of real audio recordings comprising a variety of musical genres. Our findings show that music descriptor time series exhibit a certain predictability not only for short time intervals, but also for mid-term and relatively long intervals. This fact is observed independently of the descriptor, musical facet and time series model we consider. Moreover, we show that our findings are not only of theoretical relevance but can also have practical impact. To this end we demonstrate that music predictability at relatively long time intervals can be exploited in a real-world application, namely the automatic identification of cover songs (i.e., different renditions or versions of the same musical piece). Importantly, this prediction strategy yields a parameter-free approach for cover song identification that is substantially faster, allows for reduced computational storage and still maintains highly competitive accuracies when compared to state-of-the-art systems.