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Preprints
, 2026, Continual Adaptation for Pacific Indigenous Speech Recognition, http://dx.doi.org/10.48550/arxiv.2603.06310
, 2026, Adaptive Per-Channel Energy Normalization Front-end for Robust Audio Signal Processing, http://dx.doi.org/10.48550/arxiv.2510.18206
, 2025, Characterization of Speech Similarity Between Australian Aboriginal and High-Resource Languages: A Case Study on Dharawal, http://dx.doi.org/10.48550/arxiv.2509.01419
, 2025, Exploring Length Generalization For Transformer-based Speech Enhancement, http://dx.doi.org/10.48550/arxiv.2506.06697
, 2025, Mamba in Speech: Towards an Alternative to Self-Attention, http://dx.doi.org/10.48550/arxiv.2405.12609
, 2025, Should Audio Front-ends be Adaptive? Comparing Learnable and Adaptive Front-ends, http://dx.doi.org/10.48550/arxiv.2502.03260
, 2025, Blind Estimation of Sub-band Acoustic Parameters from Ambisonics Recordings using Spectro-Spatial Covariance Features, http://dx.doi.org/10.48550/arxiv.2411.03172
, 2024, Dual-Constrained Dynamical Neural ODEs for Ambiguity-aware Continuous Emotion Prediction, http://dx.doi.org/10.48550/arxiv.2407.21344
, 2024, Binaural Selective Attention Model for Target Speaker Extraction, http://dx.doi.org/10.48550/arxiv.2406.12236
, 2024, An Exploration of Length Generalization in Transformer-Based Speech Enhancement, http://dx.doi.org/10.48550/arxiv.2406.11401
, 2024, What is Learnt by the LEArnable Front-end (LEAF)? Adapting Per-Channel Energy Normalisation (PCEN) to Noisy Conditions, http://dx.doi.org/10.48550/arxiv.2404.06702
, 2024, An Empirical Study on the Impact of Positional Encoding in Transformer-based Monaural Speech Enhancement, http://dx.doi.org/10.48550/arxiv.2401.09686
, 2021, Joint Spatio-Temporal Discretisation of Nonlinear Active Cochlear Models, http://dx.doi.org/10.48550/arxiv.2108.05993
, 2021, A Novel Markovian Framework for Integrating Absolute and Relative Ordinal Emotion Information, http://dx.doi.org/10.48550/arxiv.2108.04605
, 2019, An efficient and perceptually motivated auditory neural encoding and decoding algorithm for spiking neural networks, http://dx.doi.org/10.48550/arxiv.1909.01302