# Phase-Optimized K-SVD for Signal Extraction from Underdetermined Multichannel Sparse Mixtures

arXiv: https://arxiv.org/abs/1410.2430 · HTML: https://mlsanity.com/paper/1410.2430/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2014.

## Built on by (most cited first, 1 of 1)

- [Partially Adaptive Multichannel Joint Reduction of Ego-noise and Environmental Noise](https://mlsanity.com/paper/2303.15042/) (2023)

## Abstract (arXiv)

We propose a novel sparse representation for heavily underdetermined multichannel sound mixtures, i.e., with much more sources than microphones. The proposed approach operates in the complex Fourier domain, thus preserving spatial characteristics carried by phase differences. We derive a generalization of K-SVD which jointly estimates a dictionary capturing both spectral and spatial features, a sparse activation matrix, and all instantaneous source phases from a set of signal examples. The dictionary can then be used to extract the learned signal from a new input mixture. The method is applied to the challenging problem of ego-noise reduction for robot audition. We demonstrate its superiority relative to conventional dictionary-based techniques using recordings made in a real room.