10 accepted papers
Deploying accurate event detection on resource-constrained devices is challenged by the trade-off between performance and computational cost. While Early-Exit (EE) networks offer a solution through adaptive computation, they often fail to enforce a coherent hierarchical structure, limiting the relia…
Audio-Visual Segmentation (AVS) aims to identify, at the pixel level, the object in a visual scene that produces a given sound. Current AVS methods rely on costly fine-grained annotations of mask-audio pairs, making them impractical for scalability. To address this, we propose the Modality Correspo…
Novel view acoustic synthesis (NVAS) aims to render binaural audio at any target viewpoint, given a mono audio emitted by a sound source at a 3D scene. Existing methods have proposed NeRF-based implicit models to exploit visual cues as a condition for synthesizing binaural audio. However, in additio…
This paper addresses the challenge of improving user experience on e-commerce platforms by enhancing product ranking relevant to user’s search queries. Ambiguity and complexity of user queries often lead to a mismatch between user’s intent and retrieved product titles or documents. Recent approaches…
Sound Event Detection (SED) aims to predict the temporal boundaries of all the events of interest and their class labels, given an unconstrained audio sample. Taking either the split-and-classify (i.e., frame-level) strategy or the more principled event-level modeling approach, all existing methods…
Automatic Audio Captioning (AAC) refers to the task of describing an audio sample in a natural language (NL) text. Unlike NL text generation tasks, which rely on lexical semantic metrics like BLEU for evaluation, the AAC evaluation metric requires acoustic semantics to map NL text corresponding to s…
Lung Auscultation is a non-invasive process of distinguishing normal respiratory sounds from abnormal ones by analyzing the airflow along the respiratory tract. With developments in the Deep Learning (DL) techniques and wider access to anonymized medical data, automatic detection of specific sounds…
Dysarthria is a motor speech impairment caused by muscle weakness. Individuals, with this condition, are unable to control rapid movement of the velum leading to reduction in intelligibility, audibility, naturalness and efficiency of vocal communication. Systems that can assess intelligibility of dy…
An End-to-End model with convolutional layers and multi-head self attention mechanism is proposed for Speech Emotion Recognition (SER) task. As inputs, we propose to use both the deep encoded linguistic features that carry the language related context of emotion and the audio spectrogram that are re…
Individuals with dysarthria are unable to control rapid movement of the velum leading to reduction in intelligibility, audibility, naturalness and efficiency of vocal communication. Automatic intelligibility assessment of dysarthric patients allows clinicians diagnose the impact of therapy and medic…