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Rajat Hebbar

5 accepted papers

2024

CVAT-BWV: A Web-Based Video Annotation Platform for Police Body-Worn Video

Parsa Hejabi, Akshay Kiran Padte, Preni Golazizian, Rajat Hebbar, Jackson Trager, Georgios Chochlakis +5
IJCAI 2024poster

We introduce an open-source platform for annotating body-worn video (BWV) footage aimed at enhancing transparency and accountability in policing. Despite the widespread adoption of BWVs in police departments, analyzing the vast amount of footage generated has presented significant challenges. This i…

Cited by 1Source ↗PDF ↗code ↗Scholar ↗
2024

TRUST-SER: On The Trustworthiness Of Fine-Tuning Pre-Trained Speech Embeddings For Speech Emotion Recognition

Tiantian Feng, Rajat Hebbar, Shrikanth Narayanan
ICASSP 2024accepted

Recent studies have explored using pre-trained embeddings for speech emotion recognition, achieving comparable performance to conventional methods that rely on low-level knowledge-inspired acoustic features. These embeddings are often generated from models trained on large-scale speech datasets usin…

Cited by 0Source ↗Scholar ↗
2023

A Dataset for Audio-Visual Sound Event Detection in Movies

Rajat Hebbar, Digbalay Bose, Krishna Somandepalli, Veena Vijai, Shrikanth Narayanan
ICASSP 2023accepted

Audio event detection is a widely studied field, with applications ranging from self-driving cars to healthcare. In-the-wild datasets such as Audioset have propelled research in this field. However, many efforts typically involve manual annotation and verification, which is expensive to perform at s…

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2023

Contextually-Rich Human Affect Perception Using Multimodal Scene Information

Digbalay Bose, Rajat Hebbar, Krishna Somandepalli, Shrikanth Narayanan
ICASSP 2023accepted

The process of human affect understanding involves the ability to infer person specific emotional states from various sources including images, speech, and language. Affect perception from images has predominantly focused on expressions extracted from salient face crops. However, emotions perceived…

Cited by 0Source ↗Scholar ↗
2019

Robust Speech Activity Detection in Movie Audio: Data Resources and Experimental Evaluation

Rajat Hebbar, Krishna Somandepalli, Shrikanth S. Narayanan
ICASSP 2019accepted

Speech activity detection in highly variable acoustic conditions is a challenging task. Many approaches to detect speech activity in such conditions involve an inherent knowledge of the noise types involved. Movie audio can offer an excellent research test-bed for developing speech activity models.…

Cited by 0Source ↗Scholar ↗