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Speech enhancement in wireless acoustic sensor networks requires the exchange of audio signals. Since the wireless communication often dominates the nodes’ energy budget, techniques for data exchange reduction are crucial. Adaptive quantization aims to optimize the bit depth of each exchanged signal according to its contribution to the speech enhancement performance. This enables the network to scale its energy and communication bandwidth requirements according to the current operating environment. The impact metric was previously proposed to predict the effect of quantization in linear minimum mean squared error (MMSE) estimation. We provide new insights into greedy adaptive quantization based on this impact metric. We achieve this by expanding the mathematical framework to include a new metric based on the gradient of the MMSE as a function of the quantization noise power. Using these tools, we show how the MMSE gradient naturally leads to a greedy algorithm and how the impact metric is a generalization of the gradient metric and a previously proposed metric. Besides, we validate the impact metric for adaptive quantization both in a simulated and in a real wireless acoustic sensor network deployed in a home environment, showing the energy savings achievable through greedy adaptive quantization.

Adaptive Quantization for Multichannel Wiener Filter-Based Speech Enhancement in Wireless Acoustic Sensor Networks