Audio categorization; Audio indexing; Audio recognition
Audio classification aims at classifying a piece of audio signal into one of the pre-defined semantic classes. It is typically realized as a combination of a learning step to learn a statistical model of each semantic class, and an inference step to estimate which semantic class is closest to the given piece of audio signal.
Audio classification associates semantic labels with audio signals, and can also be referred to as audio indexing, audio categorization or audio recognition. As such, audio classification plays an important role in facilitating search and retrieval in large-scale audio collections (databases). Semantic labels are used to represent semantic classes or semantic concepts, which can be defined at different abstraction and complexity levels. Typical examples of basic semantic audio classes are speech, music, environmental sounds, and silence, which can be detected rather...
- 1.Baillie M, Jose JM. Audio-based event detection for sports video. In: Proceedings of the 2nd International Conference on Image and Video Retrieval; 2003. p. 300–9.Google Scholar
- 3.Cheng WH, Chu WT, Wu, JL. Semantic context detection based on hierarchical audio models. In: Proceedings of the 5th ACM SIGMM International Workshop on Multimedia Information Retrieval; 2003. p. 109–15.Google Scholar
- 6.Heckerman D. A tutorial on learning with Bayesian networks. Microsoft Research, Redmond, Washington, Tech. Rep. MSR-TR-95-06; 1995.Google Scholar
- 11.Moncrieff S, Dorai C, Venkatesh S. Detecting indexical signs in film audio for scene interpretation. In: Proceedings of the IEEE International Conference on Multimedia and Expo; 2001. p. 1192–5.Google Scholar
- 13.Reyes-Gomez MJ, Ellis DPW. Selection, parameter estimation, and discriminative training of hidden Markov models for general audio modeling. In: Proceedings of the IEEE International Conference on Multimedia and Expo; 2003. p. 73–6.Google Scholar
- 14.Rui Y, Gupta A, Acero A. Automatically extracting highlights for TV baseball programs. In: Proceedings of the 8th ACM International Conference on Multimedia; 2000. p. 105–15.Google Scholar
- 15.Xiong Z, Radhakrishnan R, Divakaran A, Huang TS. Audio events detection based highlights extraction from baseball, golf and soccer games in a unified framework. In: Proceedings of the IEEE International Conference on Multimedia and Expo; 2003. p. 401–4.Google Scholar
- 16.Xu M, Maddage N, Xu CS, Kankanhalli M, Tian Q. Creating audio keywords for event detection in soccer video. In: Proceedings of the IEEE International Conference on Multimedia and Expo; 2003. p. 281–4.Google Scholar
- 17.Zhang T, Jay Kuo CC. Hierarchical system for content-based audio classification and retrieval. In: Proceedings of the SPIE: Multimedia Storage and Archiving Systems III; 1998. p. 398–409.Google Scholar