2,847 research outputs found
Deep Beamforming for Speech Enhancement and Speaker Localization with an Array Response-Aware Loss Function
Recent research advances in deep neural network (DNN)-based beamformers have
shown great promise for speech enhancement under adverse acoustic conditions.
Different network architectures and input features have been explored in
estimating beamforming weights. In this paper, we propose a deep beamformer
based on an efficient convolutional recurrent network (CRN) trained with a
novel ARray RespOnse-aWare (ARROW) loss function. The ARROW loss exploits the
array responses of the target and interferer by using the ground truth relative
transfer functions (RTFs). The DNN-based beamforming system, trained with ARROW
loss through supervised learning, is able to perform speech enhancement and
speaker localization jointly. Experimental results have shown that the proposed
deep beamformer, trained with the linearly weighted scale-invariant
source-to-noise ratio (SI-SNR) and ARROW loss functions, achieves superior
performance in speech enhancement and speaker localization compared to two
baselines.Comment: 6 page
Deep Long Short-Term Memory Adaptive Beamforming Networks For Multichannel Robust Speech Recognition
Far-field speech recognition in noisy and reverberant conditions remains a
challenging problem despite recent deep learning breakthroughs. This problem is
commonly addressed by acquiring a speech signal from multiple microphones and
performing beamforming over them. In this paper, we propose to use a recurrent
neural network with long short-term memory (LSTM) architecture to adaptively
estimate real-time beamforming filter coefficients to cope with non-stationary
environmental noise and dynamic nature of source and microphones positions
which results in a set of timevarying room impulse responses. The LSTM adaptive
beamformer is jointly trained with a deep LSTM acoustic model to predict senone
labels. Further, we use hidden units in the deep LSTM acoustic model to assist
in predicting the beamforming filter coefficients. The proposed system achieves
7.97% absolute gain over baseline systems with no beamforming on CHiME-3 real
evaluation set.Comment: in 2017 IEEE International Conference on Acoustics, Speech and Signal
Processing (ICASSP
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
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