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BiEAR: A Human Auditory-Inspired Adaptive Binaural Front-end for Multi-Speaker Localisation and Distance Estimation
Hanyu Meng, Eliathamby Ambikairajah, Vidhyasaharan Sethu +2
We present BiEAR, a human auditory-inspired adaptive binaural front-end for multi-speaker localisation and distance estimation. Inspired by medial olivocochlear (MOC) feedback in h…
Why Can't They Remember? Uncovering Representation and Retrieval Bottlenecks in Multi-Turn Acoustic Memory
Yang Xiao, Siyi Wang, Han Yin +4
Large audio language models (LALMs) process both speech and environmental acoustic cues, yet struggle to retain non-speech information across multi-turn interactions. The performan…
Beyond Deep Learning: Speech Segmentation and Phone Classification with Neural Assemblies
Trevor Adelson, Vidhyasaharan Sethu, Ting Dang
Deep learning dominates speech processing but relies on massive datasets, global backpropagation-guided weight updates, and produces entangled representations. Assembly Calculus (A…
Adaptive Per-Channel Energy Normalization Front-end for Robust Audio Signal Processing
Hanyu Meng, Vidhyasaharan Sethu, Eliathamby Ambikairajah +2
In audio signal processing, learnable front-ends have shown strong performance across diverse tasks by optimizing task-specific representation. However, their parameters remain fix…
Characterization of Speech Similarity Between Australian Aboriginal and High-Resource Languages: A Case Study on Dharawal
Ting Dang, Trini Manoj Jeyaseelan, Eliathamby Ambikairajah +1
Australian Aboriginal languages are of significant cultural and linguistic value but remain severely underrepresented in modern speech AI systems. While state-of-the-art speech fou…
Should Audio Front-ends be Adaptive? Comparing Learnable and Adaptive Front-ends
Qiquan Zhang, Buddhi Wickramasinghe, Eliathamby Ambikairajah +2
Hand-crafted features, such as Mel-filterbanks, have traditionally been the choice for many audio processing applications. Recently, there has been a growing interest in learnable…