# Sound event localization and classification using WASN in Outdoor Environment

arXiv: https://arxiv.org/abs/2403.20130 · HTML: https://mlsanity.com/paper/2403.20130/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 60 references that are in the map.
- First posted on arXiv 29 Mar 2024.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Pyroomacoustics: A Python package for audio room simulations and array processing algorithms](https://mlsanity.com/paper/1710.04196/) (2017)
- [Sound Event Detection: A Tutorial](https://mlsanity.com/paper/2107.05463/) (2021)
- [Overview and Evaluation of Sound Event Localization and Detection in DCASE 2019](https://mlsanity.com/paper/2009.02792/) (2020)
- [Robust Sound Source Tracking Using SRP-PHAT and 3D Convolutional Neural Networks](https://mlsanity.com/paper/2006.09006/) (2020)
- [SALSA: Spatial Cue-Augmented Log-Spectrogram Features for Polyphonic Sound Event Localization and Detection](https://mlsanity.com/paper/2110.00275/) (2021)
- [Description on IEEE ICME 2024 Grand Challenge: Semi-supervised Acoustic Scene Classification under Domain Shift](https://mlsanity.com/paper/2402.02694/) (2024)
- [Listening for Sirens: Locating and Classifying Acoustic Alarms in City Scenes](https://mlsanity.com/paper/1810.04989/) (2018)
- [End-to-end Two-dimensional Sound Source Localization With Ad-hoc Microphone Arrays](https://mlsanity.com/paper/2210.08484/) (2022)
- [Soft Label Coding for End-to-end Sound Source Localization With Ad-hoc Microphone Arrays](https://mlsanity.com/paper/2304.07512/) (2023)
- [Microphone Subset Selection for MVDR Beamformer Based Noise Reduction](https://mlsanity.com/paper/1705.08255/) (2017)
- [Learning Multiple Sound Source 2D Localization](https://mlsanity.com/paper/2012.05515/) (2020)
- [Rate-Distributed Spatial Filtering Based Noise Reduction in Wireless Acoustic Sensor Networks](https://mlsanity.com/paper/1712.07941/) (2017)

## Built on by (most cited first, 1 of 1)

- [LoRaWAN Based Dynamic Noise Mapping with Machine Learning for Urban Noise Enforcement](https://mlsanity.com/paper/2407.21204/) (2024)

## Abstract (arXiv)

Deep learning-based sound event localization and classification is an emerging research area within wireless acoustic sensor networks. However, current methods for sound event localization and classification typically rely on a single microphone array, making them susceptible to signal attenuation and environmental noise, which limits their monitoring range. Moreover, methods using multiple microphone arrays often focus solely on source localization, neglecting the aspect of sound event classification. In this paper, we propose a deep learning-based method that employs multiple features and attention mechanisms to estimate the location and class of sound source. We introduce a Soundmap feature to capture spatial information across multiple frequency bands. We also use the Gammatone filter to generate acoustic features more suitable for outdoor environments. Furthermore, we integrate attention mechanisms to learn channel-wise relationships and temporal dependencies within the acoustic features. To evaluate our proposed method, we conduct experiments using simulated datasets with different levels of noise and size of monitoring areas, as well as different arrays and source positions. The experimental results demonstrate the superiority of our proposed method over state-of-the-art methods in both sound event classification and sound source localization tasks. And we provide further analysis to explain the reasons for the observed errors.