Audio Scene Understanding using Topic Models (bibtex)
by Kim, Samuel, Sundaram, Shiva, Georgiou, Panayiotis G. and Narayanan, Shrikanth
Abstract:
This paper introduces a method to apply the topic models in an audio scene understanding framework. Assuming that an audio signal consists of latent topics that generate acoustic words describing an audio scene, we propose to use a vector quantization method to build an acoustic word dictionary. The classification experiments with semantic labels yield promising results of using the topic models, compared to the conventional GMM-based approach, in audio scene understanding tasks.
Reference:
Audio Scene Understanding using Topic Models (Kim, Samuel, Sundaram, Shiva, Georgiou, Panayiotis G. and Narayanan, Shrikanth), In Proceedings of the Neural Information Processing Systems (NIPS) Workshop, 2009.
Bibtex Entry:
@inproceedings{kim_audio_2009,
	title = {Audio {Scene} {Understanding} using {Topic} {Models}},
	url = {http://ict.usc.edu/pubs/Audio%20Scene%20Understanding%20using%20Topic%20Models.pdf},
	abstract = {This paper introduces a method to apply the topic models in an audio scene understanding framework. Assuming that an audio signal consists of latent topics that generate acoustic words describing an audio scene, we propose to use a vector quantization method to build an acoustic word dictionary. The classification experiments with semantic labels yield promising results of using the topic models, compared to the conventional GMM-based approach, in audio scene understanding tasks.},
	booktitle = {Proceedings of the {Neural} {Information} {Processing} {Systems} ({NIPS}) {Workshop}},
	author = {Kim, Samuel and Sundaram, Shiva and Georgiou, Panayiotis G. and Narayanan, Shrikanth},
	month = dec,
	year = {2009}
}
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