.png){kind=icon}
.png){kind=icon}
Return
Bayesian Nonparametrics for Microphone Array Processing
.png){kind=icon}
OA
AI
DOI：10.1109/TASLP.2013.2294582.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Sound source localization and separation from a mixture of sounds are essential functions for computational auditory scene analysis. The main challenges are designing a unified framework for joint optimization and estimating the sound sources under auditory uncertainties such as reverberation or unknown number of sounds. Since sound source localization and separation are mutually dependent, their simultaneous estimation is required for better and more robust performance. A unified model is presented for sound source localization and separation based on Bayesian nonparametrics. Experiments using simulated and recorded audio mixtures show that a method based on this model achieves state-of-the-art sound source separation quality and has more robust performance on the source number estimation under reverberant environments.
Keywords:
Audio source separation and enhancement (AUDSSEN)
Bayesian nonparametrics
blind source separation
microphone array processing
sound source localization
spatial andmultichannel audio (AUD-SMCA)
time frequency masking
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
I
IF:
5.1
Papers:
2.6K
Citations:
1.1W