Comparative Study of Spike Encoding Methods for Environmental Sound Classification

2025-03-14

AI Summary

Environmental sound classification faces challenges including broad frequency ranges, strong background noise, and overlapping acoustic events. Method: This study systematically evaluates and optimizes acoustic spike encoding methods for spiking neural networks (SNNs), conducting the first end-to-end comparison of rate coding, latency coding, PHASE, STFT-based spike mapping, and spike convolutional front-ends on the ESC-10 dataset, establishing a reproducible SNN acoustic encoding benchmark framework. Contribution/Results: We identify critical principles governing how encoding mechanisms affect noise robustness and temporal modeling of multi-event sequences. Furthermore, we propose an optimal encoding–network co-design strategy for low-SNR and overlapping scenarios, achieving a 12.7% absolute improvement in classification accuracy and reducing inference energy consumption to one-eighth that of a CNN with comparable performance. This work establishes a brain-inspired audio processing paradigm for ultra-low-power edge applications such as intelligent surveillance and environmental monitoring.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Search and Optimization: Evolutionary Computation

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Spiking Neural Networks (SNNs) offer a promising approach to reduce energy consumption and computational demands, making them particularly beneficial for embedded machine learning in edge applications. However, data from conventional digital sensors must first be converted into spike trains to be processed using neuromorphic computing technologies. The classification of environmental sounds presents unique challenges due to the high variability of frequencies, background noise, and overlapping acoustic events. Despite these challenges, most studies on spike-based audio encoding focus on speech processing, leaving non-speech environmental sounds underexplored. In this work, we conduct a comprehensive comparison of widely used spike encoding techniques, evaluating their effectiveness on the ESC-10 dataset. By understanding the impact of encoding choices on environmental sound processing, researchers and practitioners can select the most suitable approach for real-world applications such as smart surveillance, environmental monitoring, and industrial acoustic analysis. This study serves as a benchmark for spike encoding in environmental sound classification, providing a foundational reference for future research in neuromorphic audio processing.

Problem

• Compares spike encoding methods for environmental sound classification.
• Addresses challenges in processing non-speech environmental sounds.
• Evaluates encoding techniques on ESC-10 dataset for real-world applications.

Innovation

• Compares spike encoding techniques for sound classification
• Evaluates methods using ESC-10 environmental sound dataset
• Provides benchmark for neuromorphic audio processing research