Noisy Environment-Aware Speech Enhancement for Speech Recognition in Human-Robot Interaction Application

Noise reduction (NR) is a necessary front-end in many audio applications for improving signal quality. It was shown that sparsity-promoting sensor selection potentially makes a trade-off between energy consumption and NR performance, which is rather important for large-scale wireless acoustic sensor networks (WASNs), where many sensors contribute negligibly to NR but energy consumption affects the lifetime of WASNs. This paper presents a sensor selection approach for beamforming-based NR by minimizing the total energy consumption and constraining the output noise variance. Motivated by the optimal semi-definite programming (SDP) solution and the utility-based method, we propose three low-complexity selection metrics: weighted utility, gradient, and weighted input signal-to-noise ratio (SNR). It is shown that the proposed weighted utility and gradient-based methods are near-optimal in performance but much faster than the SDP-based method, and the weighted SNR method has the lowest time complexity with a tiny performance sacrifice. Numerical results using a simulated WASN validate the superiority of the proposed approaches over conventional methods.

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Noisy Environment-Aware Speech Enhancement for Speech Recognition in Human-Robot Interaction Application

Cited by 2 publications

References 12 publications

Improving real-time voice activity detection for perceptual robotic control in noisy environment

Improving real-time voice activity detection for perceptual robotic control in noisy environment

Low-complexity energy-aware sensor selection for noise reduction in distributed microphone networks

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