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A SVM-Based Audio Event Detection System | IEEE Conference ...
https://ieeexplore.ieee.org/abstract/document/5630626#:~:text=This%20paper%20proposes%20a%20SVM-based%20method%20to%20deal,segments%20by%20moving%20from%20start%20to%20the%20end.
A SVM-Based Audio Event Detection System | IEEE …
https://ieeexplore.ieee.org/abstract/document/5630626
A SVM-Based Audio Event Detection System Abstract: This paper proposes a SVM-based method to deal with the problem of detecting audio events(cheering and applause) by audio analysis. In our framework, a sliding window is first used to pre-segment the audio stream into short segments by moving from start to the end.
A SVM-Based Audio Event Detection System | Request PDF
https://www.researchgate.net/publication/232631452_A_SVM-Based_Audio_Event_Detection_System
For detecting cheering and applause events in the audio stream of various TV programs, Lu et al. [31] proposed a method based on the SVM classifier and …
A SVM-Based Audio Event Detection System - 2010 ...
http://yadda.icm.edu.pl/yadda/element/bwmeta1.element.ieee-000005630626
Third, SVM(super vector machine) is used as the classifier to detect audio events. Finally, smoothing rules are used to eliminate the false alarms caused by background noise. By integrating all the techniques, an average F value of 79.71% is achieved in the audio detection task evaluated on nearly 8 hour TV programs.
Acoustic Event Detection: SVM-based System and Evaluation ...
https://upcommons.upc.edu/bitstream/handle/2117/9844/UPC_AED_clear_2007.pdf;sequence=3
In this paper, the Acoustic Event Detection (AED) system developed at the UPC is described, and its results in the CLEAR evaluations carried out in March 2007 are reported. The system uses a set of features composed of fre-quency-filtered band energies and perceptual features, and it is based on SVM classifiers and multi-microphone decision fusion.
Acoustic Event Detection: SVM-Based System and …
https://link.springer.com/chapter/10.1007/978-3-540-68585-2_34
In this paper, the Acoustic Event Detection (AED) system developed at the UPC is described, and its results in the CLEAR evaluations carried out in March 2007 are reported. The system uses a set of features composed of frequency-filtered band energies and perceptual features, and it is based on SVM classifiers and multi-microphone decision fusion.
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