TY - GEN
T1 - Semantic learning for audio applications
T2 - 2006 Conference on Computer Vision and Pattern Recognition Workshops
AU - Sukthankar, Rahul
AU - Ke, Yan
AU - Hoiem, Derek
PY - 2006
Y1 - 2006
N2 - Recent work in machine learning has significantly benefited semantic extraction tasks in computer vision, particularly for object recognition and image retrieval. We argue that the computer vision techniques that have been successfully applied in those settings can effectively be translated to other domains, such as audio. This claim is supported by recent results in music vs. speech classification, structure from sound, robust music identification and sound object recognition. This paper focuses on two such audio applications and demonstrates how ideas from computer vision map naturally to these problems.
AB - Recent work in machine learning has significantly benefited semantic extraction tasks in computer vision, particularly for object recognition and image retrieval. We argue that the computer vision techniques that have been successfully applied in those settings can effectively be translated to other domains, such as audio. This claim is supported by recent results in music vs. speech classification, structure from sound, robust music identification and sound object recognition. This paper focuses on two such audio applications and demonstrates how ideas from computer vision map naturally to these problems.
UR - http://www.scopus.com/inward/record.url?scp=33845596145&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33845596145&partnerID=8YFLogxK
U2 - 10.1109/CVPRW.2006.191
DO - 10.1109/CVPRW.2006.191
M3 - Conference contribution
AN - SCOPUS:33845596145
SN - 0769526462
SN - 9780769526461
T3 - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
BT - 2006 Conference on Computer Vision and Pattern Recognition Workshop
Y2 - 17 June 2006 through 22 June 2006
ER -