Enhancing Environmental Noise Management Through Siamese Convolutional Neural Network with Triplet Lost Function for Identification of Principal Sound Sources

Abstract

Identifying principal noise sources in distant fields is a challenging task due to the convergence of multiple sound sources and the degradation of acoustic signals over distance. This task becomes critical in industrial environments, where adherence to noise regulations is mandatory. Conforming to noise regulations can result in decreased productivity or even necessitate cessation of operations, significantly impacting the industry and the surrounding communities. Our goal, in response to these issues, is to develop a tool to facilitate a highly refined analysis of the causality relationship between industrially produced noise and operational productivity. Our approach is characterized by the robust identification of dominant noise sources in complex soundscapes, with a focus on specific sound events rather than locations or equipment types. To achieve this, we utilize Siamese Convolutional Neural Networks (SCNNs) with a triplet loss function. In our methodology, near-field captures from noisy equipment are used as anchors. The anchors are compared with both positive and negative instances captured by environmental noise monitoring stations in distant sound fields. Positive instances refer to those where the sound event produced by the equipment under study dominates, while negative instances are those where the anchor sound is present but not dominant. Preliminary results are promising, indicating that it is possible to discern among various near-field captures to identify the primary contributor to the noise detected outside an industrial plant. With real-time identification and loop back as the next goals, this study paves the way for more advanced noise monitoring, presenting its potential significant role in shaping noise management strategies within industrial contexts.