Multiresolution alignment for multiple unsynchronized audio sequences using Sequential Monte Carlo samplers

With proliferation of smart devices such as smart phones, it is common that an event is recorded by multiple individuals creating several audio and video perspectives. Such user generated content is mostly unorganized (not synchronized). In this work, we consider the problem of aligning of multiple unsynchronized audio sequences and propose a multiresolution alignment algorithm using Sequential Monte Carlo samplers in a course to fine structure. The proposed method is evaluated with a real-life dataset from Jiku Mobile Video Datasets and has proven to be competitive with the baseline fingerprinting based alignment methods, with the proper choice of parameters. Keywords: Multiple audio alignment, Multiresolution alignment, Audio fingerprint, Bayesian inference, Sequential Monte Carlo samplers, Sequential alignmen

Belief Propagation algorithm for Automatic Chord Estimation

International audienceThis work aims at bridging the gap between two completely distinct research fields: digital communications and Music Information Retrieval. While works in the MIR community have long used algorithms borrowed from speech signal processing, text recognition or image processing, to our knowledge very scarce work based on digital communications algorithms has been produced. This paper specifically targets the use of the Belief Propagation algorithm for the task of Automatic Chord Estimation. This algorithm is of widespread use in iterative decoders for error correcting codes and we show that it offers improved performances in ACE by genuinely incorporating the ability to take constraints between distant parts of the song into account. It certainly represents a promising alternative to traditional MIR graphical models approaches, in particular Hidden Markov Models

Dig that Lick: Exploring Patterns in Jazz Solos

International audienceWe give an overview of outcomes from the recently completed project "Dig that lick: Analysing large-scale data for melodic patterns in jazz performances", involving a multidisciplinary and international team of researchers. On the technical side, the project built infrastructure and tools for extraction, discovery, search and visualisation of melodic patterns and associated metadata. These outcomes facilitate analysis on the musicological side of the use of melodic patterns in improvisation, to answer questions about the origins, evolution and transmission of such patterns. This in turn gives insight into the extent to which improvisers rely on patterns, the development of individual and shared styles, and the level of influence of individual musicians, based on the amount of reuse of their improvised material by later musicians