2019/10/23 by Robin Scheibler, Scheibler, Robin, Nobutaka Ono +1
Computer Science · Earth and Planetary Sciences · #Audio and Speech Processing (eess.AS) #Blind Source Separation Techniques #FOS: Computer and information sciences #FOS: Electrical engineering #Signal Processing (eess.SP) #Sound (cs.SD) #Speech and Audio Processing #Underwater Acoustics Research #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1910.10654
openalex publication_date 2019/10/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
We propose fast independent vector extraction (FIVE), a new algorithm that blindly extracts a single non-Gaussian source from a Gaussian background. The algorithm iteratively computes beamforming weights maximizing the signal-to-interference-and-noise ratio for an approximate noise covariance matrix. We demonstrate that this procedure minimizes the negative log-likelihood of the input data according to a well-defined probabilistic model. The minimization is carried out via the auxiliary function technique whereas, unlike related methods, the auxiliary function is globally minimized at every iteration. Numerical experiments are carried out to assess the performance of FIVE. We find that it is vastly superior to competing methods in terms of convergence speed, and has high potential for real-time applications.