Search Results for author:
Found 8 papers, 0 papers with code
Unsupervised Audio-Visual Segmentation with Modality Alignment
Audio-Visual Segmentation (AVS) aims to identify, at the pixel level, the object in a visual scene that produces a given sound.
Sarcasm in Sight and Sound: Benchmarking and Expansion to Improve Multimodal Sarcasm Detection
The introduction of the MUStARD dataset, and its emotion recognition extension MUStARD++, have identified sarcasm to be a multi-modal phenomenon -- expressed not only in natural language text, but also through manners of speech (like tonality and intonation) and visual cues (facial expression).
Leveraging Foundation models for Unsupervised Audio-Visual Segmentation
Particularly, in situations where existing supervised AVS methods struggle with overlapping foreground objects, our models still excel in accurately segmenting overlapped auditory objects.
DiffSED: Sound Event Detection with Denoising Diffusion
In this work, we reformulate the SED problem by taking a generative learning perspective.
Text-to-Audio Grounding Based Novel Metric for Evaluating Audio Caption Similarity
Automatic Audio Captioning (AAC) refers to the task of translating an audio sample into a natural language (NL) text that describes the audio events, source of the events and their relationships.
Automatic Audio Captioning using Attention weighted Event based Embeddings
Automatic Audio Captioning (AAC) refers to the task of translating audio into a natural language that describes the audio events, source of the events and their relationships.
Automatic Speaker Independent Dysarthric Speech Intelligibility Assessment System
Dysarthria is a condition which hampers the ability of an individual to control the muscles that play a major role in speech delivery.
Semi Supervised Learning For Few-shot Audio Classification By Episodic Triplet Mining
In this paper, we propose to replace the typical prototypical loss function with an Episodic Triplet Mining (ETM) technique.
