Efficient FPGA implementation for sound source separation using direction-informed multichannel non-negative matrix factorization

The Journal of Supercomputing, vol. 80, pp. 13411–13433

Abstract

Abstract Sound source separation (SSS) is a fundamental problem in audio signal processing, aiming to recover individual audio sources from a given mixture. A promising approach is multichannel non-negative matrix factorization (MNMF), which employs a Gaussian probabilistic model encoding both magnitude correlations and phase differences between channels through spatial covariance matrices (SCM). In this work, we present a dedicated hardware architecture implemented on field programmable gate arrays (FPGAs) for efficient SSS using MNMF-based techniques. A novel decorrelation constraint is presented to facilitate the factorization of the SCM signal model, tailored to the challenges of multichannel source separation. The performance of this FPGA-based approach is comprehensively evaluated, taking advantage of the flexibility and computational capabilities of FPGAs to create an efficient real-time source separation framework. Our experimental results demonstrate consistent, high-quality results in terms of sound separation.

Authors 3

1. Antonio J. Muñoz-Montoro

   Universidad de Jaén

   Affiliation as printed

   Department of Telecommunication Engineering, University of Jaén, Avenida de la Universidad, s/n, 23700, Linares, Andalucía, Spain

2. Julio J. Carabias-Orti

   Universidad de Jaén

   Affiliation as printed

   Department of Telecommunication Engineering, University of Jaén, Avenida de la Universidad, s/n, 23700, Linares, Andalucía, Spain

3. José Ranilla

   Universidad de Oviedo

   Affiliation as printed

   Department of Computer Science, University of Oviedo, Campus de Gijón, s/n, 33203, Gijón, Asturias, Spain