A Robust Steered Response Power Localization Method for Wireless Acoustic Sensor Networks in an Outdoor Environment

Abstract

The localization of outdoor acoustic sources has attracted attention in wireless sensor networks. In this paper, the steered response power (SRP) localization of band-pass signal associated with steering time delay uncertainty and coarser spatial grids is considered. We propose a modified SRP-based source localization method for enhancing the localization robustness in outdoor scenarios. In particular, we derive a sufficient condition dependent on the generalized cross-correlation (GCC) waveform function for robust on-grid source localization and show that the SRP function with GCCs satisfying this condition can suppress the disturbances induced by the grid distance and the uncertain steering time delays. Then a GCC refinement procedure for band-pass GCCs is designed, which uses complex wavelet functions in multiple sub-bands to filter the GCCs and averages the envelopes of the filtered GCCs as the equivalent GCC to match the sufficient condition. Simulation results and field experiments demonstrate the excellent performance of the proposed method against the existing SRP-based methods.

Energy Neutral Urban Noise Monitoring and Classification With LoRaWAN Based IoT

“…Despite slightly higher edge computation requirements, TDoA based approach is an important alternative to energy based methods for urban acoustic localization especially when combined with a phase transform (GCC-PHAT) to prioritize phase over magnitude during correlation calculation. Instead of using cross-correlation to extract time delay, SRP based localization approaches calculate signal power at candidate source positions and returns the position with the highest SRP as the estimated location [43]. Since this approach requires cross-correlations to be calculated for all signal pairs, it requires audio signals from all the nodes to be collected at a central location.…”

“…Since this approach requires cross-correlations to be calculated for all signal pairs, it requires audio signals from all the nodes to be collected at a central location. In [43] the authors use 2-second audio with 10 kHz sampling rate, thus each node transmits at least 80 KB of data (using 4-byte floating points) for each localization task which would take hours to be transmitted in LPWAN-based systems.…”

Improvement of learning-based methods for localization of multiple sound sources

“…Signals of the localized sound sources range from narrowband or harmonic signals (Ding et al) to broadband noise signals (Huang et al 2021) to human speech (Diaz-Guerra et al 2021;Subramanian et al 2021) or a variety of real-world signals and noises (Adavanne et al 2019a;Huang et al 2021;Kapka, Lewandowski 2019).…”