An Analysis of Untrained Deep Reservoir Networks for Audio Surveillance
In this paper, we investigate untrained recurrent models from the Reservoir Computing (RC) paradigm for audio surveillance, focusing on bidirectional Echo State Networks with different depths, from shallow to deep configurations, for emergency sound event detection. We evaluate these models on the MIVIA Audio Events dataset in a multiclass setting across different Signal-to-Noise Ratio (SNR) levels, with the goal of assessing the trade-off between depth, recognition performance, and computational efficiency. We compare the proposed architectures against fully trained recurrent and convolutional-recurrent baselines, namely Bidirectional Long Short-Term Memory networks (BiLSTMs) and Convolu...
| Authors | Corrado Baccheschi, Patrizio Dazzi |
|---|---|
| Primary category | cs.SD |
| Published | 20 June 2026 |
| Last updated at source | 20 June 2026 |
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