arXiv subjects

Henry Zhong

Publications and source records attributed to Henry Zhong.

4 recordsLinked to original sources

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.

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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.

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arXiv2026

AuditoryHuM: Auditory Scene Label Generation and Clustering using Human-MLLM Collaboration

Manual annotation of audio datasets is labour intensive, and it is challenging to balance label granularity with acoustic separability. We introduce AuditoryHuM, a novel framework for the unsupervised discovery and clustering of auditory scene labels using a collaborative Human-Multimodal Large Language Model (MLLM) approach. By leveraging MLLMs (Gemma and Qwen) the framework generates contextually relevant labels for audio data. To ensure label quality and mitigate hallucinations, we employ zero-shot learning techniques (Human-CLAP) to quantify the alignment between generated text labels and raw audio content. A strategically targeted human-in-the-loop intervention is then used to refine the least aligned pairs. The discovered labels are grouped into thematically cohesive clusters using an adjusted silhouette score that incorporates a penalty parameter to balance cluster cohesion and thematic granularity. Evaluated across three diverse auditory scene datasets (ADVANCE, AHEAD-DS, and TAU 2019), AuditoryHuM provides a scalable, low-cost solution for creating standardised taxonomies. This solution facilitates the training of lightweight scene recognition models deployable to edge devices, such as hearing aids and smart home assistants. The project page and code: https://github.com/Australian-Future-Hearing-Initiative

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arXiv2026

A dataset and model for auditory scene recognition for hearing devices: AHEAD-DS and OpenYAMNet

Scene recognition is important for hearing devices, however; this is challenging, in part because of the limitations of existing datasets. Datasets often lack public accessibility, completeness, or audiologically relevant labels, hindering systematic comparison of machine learning models. Deploying such models on resource-constrained edge devices presents another challenge.The proposed solution is two-fold, a repack and refinement of several open source datasets to create AHEAD-DS, a dataset designed for auditory scene recognition for hearing devices, and introduce OpenYAMNet, a sound recognition model. AHEAD-DS aims to provide a standardised, publicly available dataset with consistent labels relevant to hearing aids, facilitating model comparison. OpenYAMNet is designed for deployment on edge devices like smartphones connected to hearing devices, such as hearing aids and wireless earphones with hearing aid functionality, serving as a baseline model for sound-based scene recognition. OpenYAMNet achieved a mean average precision of 0.86 and accuracy of 0.93 on the testing set of AHEAD-DS across fourteen categories relevant to auditory scene recognition. Real-time sound-based scene recognition capabilities were demonstrated on edge devices by deploying OpenYAMNet to an Android smartphone. Even with a 2018 Google Pixel 3, a phone with modest specifications, the model processes audio with approximately 50ms of latency to load the model, and an approximate linear increase of 30ms per 1 second of audio. The project website with links to code, data, and models. https://github.com/Australian-Future-Hearing-Initiative

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