Envelope and wavelet transform for sound localisation at low sampling rates in wireless sensor networks

High sampling frequencies in acoustic wireless sensor network (AWSN) are required to achieve precise sound localisation. But they are also mean analysis time and memory intensive (i.e., huge data to be processed and more memory space to be occupied which form a burden on the nodes limited resources). Decreasing sampling rates below Nyquist criterion in acoustic source localisation (ASL) applications requires development of the existing time delay estimation techniques in order to overcome the challenge of low time resolution. This work proposes using envelope and wavelet transform to enhance the resolution of the received signals through the combination of different time-frequency contents. Enhanced signals are processed using cross-correlation in conjunction with a parabolic fit interpolation to calculate the time delay accurately. Experimental results show that using this technique, estimation accuracy was improved by almost a factor of 5 in the case of using 4.8kHz sampling rate. Such a conclusion is useful for developing precise ASL without the need of any excessive sensor resources, particularly for structural health monitoring applications.