Sourish Chaudhuri

TL;DR: In this paper, the authors used various CNN architectures to classify the soundtracks of a dataset of 70M training videos (5.24 million hours) with 30,871 video-level labels.

TL;DR: This work uses various CNN architectures to classify the soundtracks of a dataset of 70M training videos with 30,871 video-level labels, and investigates varying the size of both training set and label vocabulary, finding that analogs of the CNNs used in image classification do well on the authors' audio classification task, and larger training and label sets help up to a point.

TL;DR: Non-negative matrix factorization based speech enhancement in robust automatic recognition of mixtures of speech and music is proposed and shown to produce a consistent, significant improvement on the recognition performance in the comparison with the baseline method.

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.

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.