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Evaluations on 12 h of audio data indicate that the proposed framework can achieve satisfying results, both on key audio effect detection and auditory context inference.
In addition, a novel blackhole detection mechanism is also proposed.
In this article, we present an improved audio steganography approach that reduces distortion of the stego audio.
Moreover, it introduces a general framework to audio segmentation, which does not depend explicitly on the number of audio classes.
Open accessProceedings ArticleDOI
15 Apr 2018
64 Citations
Experimental results on different types of audio signal confirm the potential of the proposed approach.
Open accessBook ChapterDOI
Ruohan Gao, Rogerio Feris, Kristen Grauman 
08 Sep 2018
217 Citations
Our work is the first to learn audio source separation from large-scale "in the wild" videos containing multiple audio sources per video.
Comparison with existing blackhole detection technique [6] reveals that the proposed technique obtains better results.
Open accessProceedings ArticleDOI
Gautam Bhattacharya, Philippe Depalle 
04 May 2014
9 Citations
We present experimental results on a wide range of audio signals, and show that the method is able to yield results thats are competitive with other audio denosing approaches.