Multi-channel bayesian background noise suppression using perceptual cost functions

This paper proposes a frequency-based approach for background noise suppression with consideration for human psychoacoustics. The approach utilizes a perceptual cost function analysis based on temporal masking thresholds. By optimizing the cost function, the concept eliminates background noises that mask the original signals, while maintaining the minimum perceptual distortion of the original signals. The perceptual cost function can also be implemented within a Gibbs sampling framework, which better models the uncertainty within the original signal. These approaches improve existing noise reduction techniques, enhancing perceived audio quality (PEAQ), mean opinion score (MOS), and signal to noise ratio (SNR).