Spike Encoding for Environmental Sound: A Comparative Benchmark

Spiking Neural Networks (SNNs) offer energy efficient processing suitable for edge applications, but conventional sensor data must first be converted into spike trains for neuromorphic processing. Environmental sound, including urban soundscapes, poses challenges due to variable frequencies, background noise, and overlapping acoustic events, while most spike based audio encoding research has focused on speech. This paper analyzes three spike encoding methods, Threshold Adaptive Encoding (TAE), Step Forward (SF), and Moving Window (MW) across three datasets: ESC10, UrbanSound8K, and TAU Urban Acoustic Scenes. Our multiband analysis shows that TAE consistently outperforms SF and MW in reconstruction quality, both per frequency band and per class across datasets. Moreover, TAE yields the lowest spike firing rates, indicating superior energy efficiency. For downstream environmental sound classification with a standard SNN, TAE also achieves the best performance among the compared encoders. Overall, this work provides foundational insights and a comparative benchmark to guide the selection of spike encoders for neuromorphic environmental sound processing.