A Repetition-based Triplet Mining Approach for Music Segmentation

Contrastive learning has recently appeared as a well-suited method to find representations of music audio signals that are suitable for structural segmentation. However, most existing unsupervised training strategies omit the notion of repetition and therefore fail at encompassing this essential aspect of music structure. This work introduces a triplet mining method which explicitly considers repeating sequences occurring inside a music track by leveraging common audio descriptors. We study its impact on the learned representations through downstream music segmentation. Because musical repetitions can be of different natures, we give further insight on the role of the audio descriptors employed at the triplet mining stage as well as the trade-off existing between the quality of the triplets mined and the quantity of unlabelled data used for training. We observe that our method requires less non-annotated data while remaining competitive against other unsupervised methods trained on a larger corpus

A Music Structure Informed Downbeat Tracking System Using Skip-Chain Conditional Random Fields And Deep Learning

International audienc

A Study on Neural Models for Target-Based Computer-Assisted Musical Orchestration

International audienc

Dig That Lick: Exploring Patterns in Jazz with Computational Methods

International audienc

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