Identification of versions of the same musical composition by processing audio descriptions

Automatically making sense of digital information, and specially of music digital documents, is an important problem our modern society is facing. In fact, there are still many tasks that, although being easily performed by humans, cannot be effectively performed by a computer. In this work we focus on one of such tasks: the identification of musical piece versions (alternate renditions of the same musical composition like cover songs, live recordings, remixes, etc.). In particular, we adopt a computational approach solely based on the information provided by the audio signal. We propose a system for version identification that is robust to the main musical changes between versions, including timbre, tempo, key and structure changes. Such a system exploits nonlinear time series analysis tools and standard methods for quantitative music description, and it does not make use of a specific modeling strategy for data extracted from audio, i.e. it is a model-free system. We report remarkable accuracies for this system, both with our data and through an international evaluation framework. Indeed, according to this framework, our model-free approach achieves the highest accuracy among current version identification systems (up to the moment of writing this thesis). Model-based approaches are also investigated. For that we consider a number of linear and nonlinear time series models. We show that, although model-based approaches do not reach the highest accuracies, they present a number of advantages, specially with regard to computational complexity and parameter setting. In addition, we explore post-processing strategies for version identification systems, and show how unsupervised grouping algorithms allow the characterization and enhancement of the output of query-by-example systems such as the version identification ones. To this end, we build and study a complex network of versions and apply clustering and community detection algorithms. Overall, our 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 our steps are guided by the nature of the considered signals (music recordings) and the characteristics of the task at hand (version identification), we believe our methodology can be easily transferred to other contexts and domains.