Noise reduction using multi-feature cluster tracker
US9008329B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 8, 2012 |
| Grant date | Apr 14, 2015 |
| Priority date | — |
| Expiry date | Jun 8, 2032 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG10L2021/02166
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Provided are methods and systems for noise suppression within multiple time-frequency points of spectral representations. A multi-feature cluster tracker is used to track signal and noise sources and to predict signal versus noise dominance at each time-frequency point. Multiple features, such as binaural and monaural features, may be used for these purposes. A Gaussian mixture model (GMM) is developed and, in some embodiments, dynamically updated for distinguishing signal from noise and performing mask-based noise reduction. Each frequency band may use a different GMM or share a GMM with other frequency bands. A GMM may be combined from two models, with one trained to model time-frequency points in which the target dominates and another trained to model time-frequency points in which the noise dominates. Dynamic updates of a GMM may be performed using an expectation-maximization algorithm in an unsupervised fashion.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.