4 papers
Adapting offline models to a streaming context for music source separation
Dylan Sechet, Marc Evrard, Matthieu Kowalski
Real-time music source separation must satisfy two constraints: a bound on algorithmic latency and a bound on computational cost. Offline separators are usually omitted from real-t…
Simulation-Based Inference for Plate Reverb System Identification
Dylan Sechet, Marc Evrard, Matthieu Kowalski
We address Task A of the 1st DAFx Parameter Estimation Challenge, which aims to retrieve the physical parameters of a plate model from an impulse response. To do so, we use the Sim…
A Hierarchical Deep Learning Approach for Minority Instrument Detection
Dylan Sechet, Francesca Bugiotti, Matthieu Kowalski +2
Identifying instrument activities within audio excerpts is vital in music information retrieval, with significant implications for music cataloging and discovery. Prior deep learni…
Revisiting CHAMPAGNE: Sparse Bayesian Learning as Reweighted Sparse Coding
Dylan Sechet, Matthieu Kowalski, Samy Mokhtari +1
This paper revisits the CHAMPAGNE algorithm within the Sparse Bayesian Learning (SBL) framework and establishes its connection to reweighted sparse coding. We demonstrate that the…