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- Title
Efficient FPGA implementation for sound source separation using direction-informed multichannel non-negative matrix factorization.
- Authors
Diel, Philipp; Muñoz-Montoro, Antonio J.; Carabias-Orti, Julio J.; Ranilla, Jose
- 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.
- Subjects
AUDITORY scene analysis; MATRIX decomposition; NONNEGATIVE matrices; FIELD programmable gate arrays; COVARIANCE matrices; SOURCE code
- Publication
Journal of Supercomputing, 2024, Vol 80, Issue 9, p13411
- ISSN
0920-8542
- Publication type
Article
- DOI
10.1007/s11227-024-05945-w