Adaptive Decorrelation Filtering Research Articles

TWO MICROPHONE NOISE REDUCTION SYSTEM

A two microphone noise reduction system is described. In an embodiment, input signals from each of the microphones are divided into subbands and each subband is then filtered independently to separate noise and desired signals and to suppress non-stationary and stationary noise. Filtering methods used include adaptive decorrelation filtering. A post-processing module using adaptive noise cancellation like filtering algorithms may be used to further suppress stationary and non-stationary noise in the output signals from the adaptive decorrelation filtering and a single microphone noise reduction algorithm may be used to further provide optimal stationary noise reduction performance of the system.

Fast Noise Compensation and Adaptive Enhancement for Speech Separation

We propose a novel approach to improve adaptive decorrelation filtering- (ADF-) based speech source separation in diffuse noise. The effects of noise on system adaptation and separation outputs are handled separately. First, fast noise compensation (NC) is developed for adaptation of separation filters, forcing ADF to focus on source separation; next, output noises are suppressed by speech enhancement. By tracking noise components in output cross-correlation functions, the bias effect of noise on the system adaptation objective function is compensated, and by adaptively estimating output noise autocorrelations, the speech separation output is enhanced. For fast noise compensation, a blockwise fast ADF (FADF) is implemented. Experiments were conducted on real and simulated diffuse noises. Speech mixtures were generated by convolving TIMIT speech sources with acoustic path impulse responses measured in a real room with reverberation time T60 = 0.3 second. The proposed techniques significantly improved separation performance and phone recognition accuracy of ADF outputs.

Speedup convergence and reduce noise for enhanced speech separation and recognition

Novel techniques are proposed to enhance time-domain adaptive decorrelation filtering (ADF) for separation and recognition of cochannel speech in reverberant room conditions. The enhancement techniques include whitening filtering on cochannel speech to improve condition of adaptive estimation, block-iterative formulation of ADF to speed up convergence, and integration of multiple ADF outputs through post filtering to reduce reverberation noise. Experimental data were generated by convolving TIMIT speech with acoustic path impulse responses measured in real room environment, with approximately 2 m microphone-source distance and initial target-to-interference ratio of about 0 dB. The proposed techniques significantly improved ADF convergence rate, target-to-interference ratio, and accuracy of phone recognition

Subband-based adaptive decorrelation filtering for co-channel speech separation

A subband-based adaptive decorrelation filtering algorithm (SBADF) is proposed for co-channel speech separation. The SBADF decomposes the input signals into several frequency subbands, and uses the adaptive decorrelation filtering algorithm (ADF) to process the signals in each subband independently. The processed subband signals are then combined for each channel to form the separated speech. Experimental results show that while the fullband ADF can achieve better separation performance after reaching convergence, the SBADF has the advantage of improved convergence rate and reduced computational complexity by a factor of approximately two.