This paper proposes EscFOA, a framework that uses geometry-aware spatial audio to enhance immersive educational environments for visually impaired learners, outperforming conventional audio methods.

This paper conducts subjective assessments to evaluate the preference and spatial audio attributes of headrest-integrated speakers for immersive audio scenarios in automobiles.

ImmersiveTTS is an environment-aware text-to-speech model that generates natural speech seamlessly integrated within environmental contexts by explicitly modeling cross-modal interactions, achieving s…

The paper introduces an agentic soundscape construction framework for controllable compositional audio generation, which makes explicit the scene planning, source selection, temporal layout, and rende…

EigeNet introduces a geometry-informed multi-modal Transformer framework to achieve state-of-the-art few-shot novel view Room Impulse Response (RIR) prediction by effectively integrating spatial geome…

This paper presents a practical pipeline for designing and deploying large-scale Mixed Reality art exhibitions using SLAM-based alignment, and evaluates its impact on technical stability and user expe…

This paper validates a Bayesian sound localisation model using statistical methods and compares four HRTF template interpolation methods.

This paper proposes AT2SELD framework to extend pretrained GP-AT models for spatially grounded Sound Event Localization and Detection using spectral FOA descriptors, NAS, and calibration.

The paper introduces Noise-Aware BEATs (NABEATs), a noise-aware audio self-supervised learning framework that estimates clean BEATs representations from noisy audio signals using an auxiliary referenc…

The paper introduces COMET, a novel PLS-SVD framework, to analyze the audio-text modality gap in CLAP models, showing that shared concepts are captured by a small subset of axes, and proposes a spectr…

MOSS-Audio is a unified audio-language model designed for comprehensive understanding of speech, environmental sounds, and music, achieving strong performance across various audio-grounded tasks.

This paper proposes material explainability as a way to make AI models accessible and inclusive design materials for artists, designers, and makers, using a case study of building a repository for neu…