The Robotics Institute

Carnegie Mellon Robotics Institute

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Abstract

The ability to identify sounds in complex audio environ-ments is highly useful for multimedia retrieval, security, and many mobile robotic applications, but very little work has been done in this area. We present the SOLAR sys-tem, a system capable of finding sound objects, such as dog barks or car horns, in complex audio data extracted from movies. SOLAR avoids the need for segmentation by scanning over the audio data in fixed increments and clas-sifying each short audio window separately. SOLAR em-ploys boosted decision tree classifiers to select suitable features for modeling each sound object and to discrimi-nate between the object of interest and all other sounds. We demonstrate the effectiveness of our approach with experiments on thirteen sound object classes trained using only tens of positive examples and tested on hours of audio data extracted from popular movies.

Keywords

sound detection, sound retrieval

Notes

Sponsor: Intel Research Pittsburgh

Associated Center(s) / Consortia: Vision and Autonomous Systems Center

Associated Lab(s) / Group(s): Face Group

Number of pages: 4

Text Reference

Derek Hoiem, Yan Ke, and Rahul Sukthankar, "SOLAR: Sound Object Localization and Retrieval in Complex Audio Environments," IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), March, 2005, pp. 429 - 432.

BibTeX Reference

@inproceedings{Hoiem_2005_5006,
   author = "Derek Hoiem and Yan Ke and Rahul Sukthankar",
   title = "SOLAR: Sound Object Localization and Retrieval in Complex Audio Environments",
   booktitle = "IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)",
   pages = "429 - 432",
   month = "March",
   year = "2005",
   volume = "5",
}

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