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eess.AS2026

Summary of DCASE 2026 Task 5: Audio-Dependent Question Answering

Haolin He, Renhe Sun, Zheqi Dai +16

DCASE~2026 Task~5 introduces Audio-Dependent Question Answering (ADQA), which tests whether large audio-language models answer from the audio rather than from textual priors. An Au…

eess.AS2026

Efficient Text-to-Audio Generation via Pruning

Arshdeep Singh, Yi Yuan, Yun Chen +2

The paper applies filter‑based pruning to the U‑Net backbone of the AudioLDM text‑to‑audio diffusion model, reducing most of its parameters and compute while preserving generation…

eess.AS2026

Explainable AI in Speaker Recognition -- Making Latent Representations Understandable

Yanze Xu, Wenwu Wang, Mark D. Plumbley

Neural networks can be trained to learn task-relevant representations from data. Understanding how these networks make decisions falls within the Explainable AI (XAI) domain. This…

eess.AS2026

Explainable AI in Speaker Recognition -- Attention Map Visualisation and Evaluation

Yanze Xu, Mark D. Plumbley, Wenwu Wang

Explaining and understanding the decision-making process of artificial intelligence (AI) systems, particularly those implemented by neural networks, falls within the field of expla…

eess.AS2026

BioDCASE 2026 Challenge Baseline for Cross-Domain Mosquito Species Classification

Yuanbo Hou, Vanja Zdravkovic, Marianne Sinka +5

Mosquito-borne diseases affect more than one billion people each year and cause close to one million deaths. Traditional surveillance methods rely on traps and manual identificatio…

eess.AS2026

Measuring Audio's Impact on Correctness: Audio-Contribution-Aware Post-Training of Large Audio Language Models

Haolin He, Xingjian Du, Renhe Sun +16

Large Audio Language Models (LALMs) represent an important frontier in multimodal AI, addressing diverse audio tasks. Recently, post-training of LALMs has received increasing atten…