Efficient acoustic noise suppression for audio signals.

摘要

Acoustic Noise Suppression (ANS) are crucial for a variety of applications, such as audio communication, hearing aids and speech recognition. Although there have been intensive studies on ANS using microphone array, monaural and binaural ANS still presents many challenges in real-world applications. This dissertation proposes several efficient monaural and binaural noise suppression schemes to deal with two common types of acoustic noise: the additive noise and the convolutive noise in the form of reverberation.; For monaural ANS, we propose a two-stage approach by using APA algorithm and CMA algorithm to suppress the ambient noise and convolutive noise successively. The method causes zero transmission delay by a novel design and application of Real-valued Delayless Subband Adaptive Filter (RDSAF) structure. To further improve the dereverberation performance, the modified CMA algorithm is investigated and operated in the LP residual domain. Simulation results demonstrate that our method is efficient and achieves high-quality noise suppression with no delay, making it attractive for real-time applications.; To reduce additive binaural noises, we propose a new framework by integrating the merits of binaural analysis with various additive noise reduction techniques. In particular, we consider two commonly-used techniques: the perceptually motivated spectral subtraction and the subband intermittent Adaptive Noise Cancellation (ANC). In both methods, we replace the conventional VAD by a simplified binaural model to improve the voice activity detection at low SNR. This, together with some other novel modifications to take account of the non-uniform spectrum of most real-world noise, enable us to achieve enhanced performance in reducing high colored binaural noise with low SNR. Furthermore, each method also presents some unique properties, making it appropriate for different types of applications.; Finally, an adaptive binaural dereverberation strategy is proposed. The utilization of constrained Least-Squares algorithms enables it to blindly identify the left/right channel Impulse Response (IR) both efficiently and adaptively. RDSAF structure is incorporated to further improve the efficiency. Simulations show that for short channel IRs, our method can achieve almost perfect dereverberation; while for long IRs, it achieves a good dereverberation performance with only slight transmission delay.