Frequency-domain distributed multichannel wiener filtering speech enhancement algorithm

A frequency-domain distributed microphone multi-channel Wiener filter speech enhancement algorithm is proposed in this paper. In this paper, the distributed microphone speech model is considered. First, the speech signal in the time domain is converted into the speech signal in the frequency domain by the discrete Fourier transform method. Then, the unconstrained minimization problem of the noise reduction and speech distortion of the complex linear filter in the frequency domain is established. Simulation results show that the proposed algorithm is superior to some existing multi-channel speech enhancement algorithms

Improved change prediction for combined beamforming and echo cancellation with application to a generalized sidelobe canceler

Adaptive beamforming and echo cancellation are often necessary in hands-free situations in order to enhance the communication quality. Unfortunately, the combination of both algorithms leads to problems. Performing echo cancellation before the beamformer (AEC-first) leads to a high complexity. In the other case (BF-first) the echo reduction is drastically decreased due to the changes of the beam-former, which have to be tracked by the echo canceler. Recently, the authors presented the directed change prediction algorithm with directed recovery, which predicts the effective impulse response after the next beamformer change and therefore allows to maintain the low complexity of the BF-first structure and to guarantee a robust echo cancellation. However, the algorithm assumes an only slowly changing acoustical environment which can be problematic in typical time-variant scenarios. In this paper an improved change prediction is presented, which uses adaptive shadow filters to reduce the convergence time of the change prediction. For this enhanced algorithm, it is shown how it can be applied to more advanced beamformer structures like the generalized sidelobe canceler and how the information provided by the improved change prediction can also be used to enhance the performance of the overall interference cancellation

Audio source separation into the wild

International audienceThis review chapter is dedicated to multichannel audio source separation in real-life environment. We explore some of the major achievements in the field and discuss some of the remaining challenges. We will explore several important practical scenarios, e.g. moving sources and/or microphones, varying number of sources and sensors, high reverberation levels, spatially diffuse sources, and synchronization problems. Several applications such as smart assistants, cellular phones, hearing aids and robots, will be discussed. Our perspectives on the future of the field will be given as concluding remarks of this chapter

Microphone Array Speech Enhancement Via Beamforming Based Deep Learning Network

In general, in-car speech enhancement is an application of the microphone array speech enhancement in particular acoustic environments. Speech enhancement inside the moving cars is always an interesting topic and the researchers work to create some modules to increase the quality of speech and intelligibility of speech in cars. The passenger dialogue inside the car, the sound of other equipment, and a wide range of interference effects are major challenges in the task of speech separation in-car environment. To overcome this issue, a novel Beamforming based Deep learning Network (Bf-DLN) has been proposed for speech enhancement. Initially, the captured microphone array signals are pre-processed using an Adaptive beamforming technique named Least Constrained Minimum Variance (LCMV). Consequently, the proposed method uses a time-frequency representation to transform the pre-processed data into an image. The smoothed pseudo-Wigner-Ville distribution (SPWVD) is used for converting time-domain speech inputs into images. Convolutional deep belief network (CDBN) is used to extract the most pertinent features from these transformed images. Enhanced Elephant Heard Algorithm (EEHA) is used for selecting the desired source by eliminating the interference source. The experimental result demonstrates the effectiveness of the proposed strategy in removing background noise from the original speech signal. The proposed strategy outperforms existing methods in terms of PESQ, STOI, SSNRI, and SNR. The PESQ of the proposed Bf-DLN has a maximum PESQ of 1.98, whereas existing models like Two-stage Bi-LSTM has 1.82, DNN-C has 1.75 and GCN has 1.68 respectively. The PESQ of the proposed method is 1.75%, 3.15%, and 4.22% better than the existing GCN, DNN-C, and Bi-LSTM techniques. The efficacy of the proposed method is then validated by experiments