Abishek Soti | Searcharxiv

arXiv2026

Bio-inspired efficient cyclostationary analysis in machine and underwater acoustic recordings

We propose a bio-inspired approach that uses the inner-hair-cell (IHC) response of the Cascade of Asymmetric Resonators with Fast-Acting Compression (CARFAC) model to efficiently extract cyclic modulation from acoustic signals. We further investigate the contribution of IHC processing by comparing the CARFAC-IHC response with the CARFAC basilar-membrane (BM) filtering. Furthermore, the CARFAC-IHC and CARFAC-BM approach are benchmarked against conventional FFT Accumulation Method (FAM), Integrated Cyclic Modulation Coherence (ICMC), and Detection of Envelope Modulation On Noise (DEMON) approaches using the Case Western Reserve University (CWRU) bearing dataset and a real ShipsEar work-vessel recording dataset. The results demonstrate reliable recovery of characteristic cyclic components while substantially reducing the computational burden of conventional cyclostationary analysis.

eess.SP↗

arXiv2026

Towards Deployable Underwater Vessel Classification

We propose a compact underwater acoustic classification framework combining multi-representation feature engineering, temporal statistical pooling, and compact convolutional architectures designed for acoustic time-frequency and cochlear representations. We investigate multiple conventional and auditory-inspired representations and first evaluate lightweight classifiers and Conventional Neural Networks (CNNs) on ShipsEar dataset. On the provided split, a two-layer CNN achieves a macro F1 of 0.9918, while a Radial Basis Function Support Vector Machine (RBF-SVM) reaches 0.9883. However, source-recording provenance cannot be reconstructed, preventing verification of recording-independent generalisation. We therefore evaluate on DeepShip dataset using recording-level partitioning before segmentation. Under this protocol, a 157K-parameter compact CNN achieves a test macro F1 of 0.7226, while an 11.17M-parameter ResNet18 provides no improvement in validation performance under the matched setting. These results demonstrate the importance of representation-aware feature and model design, together with rigorous recording-level evaluation, for classification performance and deployability in compact underwater acoustic systems.

cs.SD↗