Adaptive distributed noise reduction for speech enhancement in wireless acoustic sensor networks
An adaptive distributed noise reduction algorithm for speech enhancement is considered, which operates in a wireless acoustic sensor network where each node collects multiple microphone signals. In previous work, it was shown theoretically that for a stationary scenario, the algorithm provides the same signal estimators as the centralized multi-channel Wiener filter, while significantly compressing the data that is transmitted between the nodes. Here, we present simulation results of a fully adaptive implementation of the algorithm, in a non-stationary acoustic scenario with a moving speaker and two babble noise sources. The algorithm is implemented using a weighted overlap-add technique to reduce the overall input-output delay. It is demonstrated that good results can be obtained by estimating the required signal statistics with a long-term forgetting factor without downdating, even though the signal statistics change along with the iterative filter updates. It is also demonstrated that simultaneous node updating provides a significantly smoother and faster tracking performance compared to sequential node updating.
Summary
This paper presents an adaptive, distributed noise reduction algorithm for speech enhancement in wireless acoustic sensor networks where each node has multiple microphones. It describes a fully adaptive implementation (including WOLA-based block processing for low latency), demonstrates equivalence to the centralized multi-channel Wiener filter in stationary cases, and evaluates performance in a nonstationary scenario with a moving speaker and babble noise.
Key Takeaways
- Understand how a distributed algorithm can replicate centralized multi-channel Wiener filter performance for stationary scenarios
- Implement block-based, low-latency processing using weighted overlap-add (WOLA) within an adaptive distributed framework
- Apply inter-node data compression strategies to drastically reduce transmitted data while maintaining enhancement quality
- Tune adaptive filter parameters to track moving sources and nonstationary babble noise in a wireless acoustic sensor network
- Evaluate distributed versus centralized solutions using objective speech-quality and noise-reduction metrics
Who Should Read This
DSP researchers and engineers with an intermediate-to-advanced background in audio/speech processing, wireless sensor networks, or real-time systems who need distributed, low-latency noise reduction techniques.
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