S
Sharadh Ramaswamy
Researcher at Google
Publications - 4
Citations - 167
Sharadh Ramaswamy is an academic researcher from Google. The author has contributed to research in topics: Speaker diarisation & Speech enhancement. The author has an hindex of 4, co-authored 4 publications receiving 102 citations.
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AVA-ActiveSpeaker: An Audio-Visual Dataset for Active Speaker Detection
Joseph Roth,Sourish Chaudhuri,Ondrej Klejch,Radhika Marvin,Andrew C. Gallagher,Liat Kaver,Sharadh Ramaswamy,Arkadiusz Stopczynski,Cordelia Schmid,Zhonghua Xi,Caroline Pantofaru +10 more
TL;DR: This paper presents the AVA Active Speaker detection dataset (AVA-ActiveSpeaker), which has been publicly released to facilitate algorithm development and comparison, and introduces a state-of-the-art, jointly trained audio-visual model for real-time active speaker detection and compares several variants.
Proceedings ArticleDOI
Ava Active Speaker: An Audio-Visual Dataset for Active Speaker Detection
Joseph Roth,Sourish Chaudhuri,Ondrej Klejch,Radhika Marvin,Andrew C. Gallagher,Liat Kaver,Sharadh Ramaswamy,Arkadiusz Stopczynski,Cordelia Schmid,Zhonghua Xi,Caroline Pantofaru +10 more
TL;DR: The AVA Active Speaker dataset (AVA-ActiveSpeaker) as discussed by the authors contains temporally labeled face tracks in videos, where each face instance is labeled as speaking or not, and whether the speech is audible.
Proceedings ArticleDOI
Supplementary Material: AVA-ActiveSpeaker: An Audio-Visual Dataset for Active Speaker Detection
Joseph Roth,Zhonghua Xi,Caroline Pantofaru,Sourish Chaudhuri,Ondrej Klejch,Radhika Marvin,Andrew C. Gallagher,Liat Kaver,Sharadh Ramaswamy,Arkadiusz Stopczynski,Cordelia Schmid +10 more
TL;DR: This paper presents the AVA Active Speaker detection dataset (AVA-ActiveSpeaker) which has been publicly released to facilitate algorithm development and comparison and introduces a state-of-the-art approach for real-time active speaker detection and compares several variants.
Patent
Collage of interesting moments in a video
TL;DR: In this paper, a computer-implemented method is proposed to determine interesting moments in a video and then generate video segments based on the interesting moments, wherein each of the video segments includes at least one interesting moment from the video.