In order to improve the speech enhancement effect of small microphone array, a better method was proposed for small-array speech enhancement by combining the Array Crosstalk Resistant Adaptive Noise Cancellation (ACRANC) method with the BeamForming (BF) method. Firstly, ACRANC subsystems were constructed to obtain multiple channels of enhanced speech signals. Then, the proposed Adaptive Mode Control (AMC) algorithm and the Delay And Sum (DAS) beamforming method were applied to the enhanced speech signals for further improving the enhancement effect of multi-channel speech signals. The computational complexity of the proposed method was estimated, and it was verified that the proposed method was able to be realized in real-time with common chips. Experimental results in actual environments show that the speech enhancement effect of the proposed method is higher than that of the ACRANC method and thus the method has some advantages.

In order to improve the denoising performance of dual mini micro-array speech enhancement system in multi-noise environment, an improved generalized sidelobe canceller speech enhancement algorithm for dual mini micro-array was proposed. According to the structure characteristics of the dual mini micro-array, firstly, an improved coherent filtering algorithm based on noise cross-power spectrum estimation was used to eliminate the weak correlation noise between microphones with long distances. Secondly, the strong correlation noise between microphones with short distances was eliminated by using a generalized sidelobe cancelling algorithm. Finally, the minima-controlled recursive averaging based sub-band spectrum subtraction was used to eliminate the residual noise in different spectrum bands purposefully. Experimental results show that the proposed algorithm achieves better score in perceptual evaluation of speech quality than existing dual mini micro-array speech enhancement algorithms under multi-noise environment, and improves the suppression effect of dual mini micro-array speech enhancement system on complex noise to a certain extent.

In order to solve the low speech recognition rate in noise environment, and the difficulty of traditional beamforming algorithm in dealing with spatial noise problem, an improved Minimum Variance Distortionless Response (MVDR) beamforming method based on dual micro-array was proposed. Firstly, the gain of micro-array was increased by diagonal loading, and the computational complexity was reduced by the inversion of recursive matrix. Then, through the modulation domain spectrum subtraction for further processing, the problem that music noise was easily produced by general spectral subtraction was solved, effectively reducing speech distortion, and well suppressing the noise. Finally, the Convolution Neural Network (CNN) was used to train the speech model and extract the deep features of speech, effectively solve the problem of speech signal diversity. The experimental results show that the proposed method achieves good recognition effect in the CNN trained speech recognition system, and has the speech recognition accuracy of 92.3% in F16 noise environment with 10 dB signal-to-noise ratio, means it has good robustness.

In order to get better speech enhancement effect for hearing aids when used in the environment with non-stationary or multiple noise, which will lead a sharp decline effect of user experience, a Coherent Filter Generalized Sidelobe Canceller (CF-GSC) speech enhancement algorithm based on small size microphone array was proposed. Aiming at the weak correlation noise which caused by the waves, fans and other approximate white noise, as well as the strong correlation noise caused by the point or other competitive sources, coherent filtering and traditional Generalized Sidelobe Canceller (GSC) structure were utilized to remove weak correlation and strong correlation noise separately, the Voice Activity Detection (VAD) algorithm was also applied during this process. The simulation results show that the proposed algorithm can obtain enhancement effect by almost 2 dB compared with the improved coherent filter and traditional generalized sidelobe canceller method under the environment of a variety of noise, meanwhile, the speech intelligibility also gets obviously improved.