Unsupervised detection of music boundaries by time series structure features
Publication Type:Conference Paper
Source:AAAI Conf. on Artificial Intelligence, AAAI Press, Toronto, Canada, p.1613-1619 (2012)
Keywords:Time Series Structure; Features
Locating boundaries between coherent and/or repetitive segments of a time series is a challenging problem pervading many scientific domains. In this paper we propose an unsupervised method for boundary detection, combining three basic principles: novelty, homogeneity, and repetition. In particular, the method uses what we call structure features, a representation encapsulating both local and global properties of a time series. We demonstrate the usefulness of our approach in detecting music structure boundaries, a task that has received much attention in recent years and for which exist several benchmark datasets and publicly available annotations. We find our method to significantly outperform the best accuracies published so far. Importantly, our boundary approach is generic, thus being applicable to a wide range of time series beyond the music and audio domains.
Identification of versions of the same musical composition: audio content-based approaches and post-processing steps
Source:LAP Lambert Academic Publishing, Saarbrücken, Germany (2011)
Keywords:Music; Information Retrieval; Time series; Complex networks; Similarity
This book focuses on the automatic identification of musical piece versions (alternate renditions of the same musical composition like cover songs, live recordings, remixes, etc.). In particular, two core approaches for version identification are proposed: model-free and model-based. Furthermore, the book introduces the use of post-processing strategies to improve the identification of versions in a query-by-example paradigm. Overall, several tools and concepts are employed, including nonlinear signal analysis, complex networks, and time series models. This work brings automatic version identification to an unprecedented stage where high accuracies are achieved and, at the same time, explores promising directions for future research. Although the main steps are guided by the nature of the considered signals (music recordings) and the characteristics of the task at hand (version identification), the methodology of this book can be easily transferred to other contexts and domains.