Random forest classification based acoustic event detection utilizing contextual-information and bottleneck features
作者：
Highlights：
• A random forest classification based acoustic event detection system was constructed as the baseline system.
• Contextual information was employed to cope with the acoustic signals with long duration.
• Global bottleneck features were employed in the acoustic event detection system to utilize the prior knowledge of the event category information.
• Category-specific bottleneck features were employed in the acoustic event detection system to utilize the prior knowledge of the event boundary information.
• Evaluations on the UPC-TALP and ITC-IRST databases of highly.
摘要
•A random forest classification based acoustic event detection system was constructed as the baseline system.•Contextual information was employed to cope with the acoustic signals with long duration.•Global bottleneck features were employed in the acoustic event detection system to utilize the prior knowledge of the event category information.•Category-specific bottleneck features were employed in the acoustic event detection system to utilize the prior knowledge of the event boundary information.•Evaluations on the UPC-TALP and ITC-IRST databases of highly.
论文关键词：Acoustic event detection,Contextual information,Global bottleneck features,Category-specific bottleneck features
论文评审过程：Received 18 March 2017, Revised 22 February 2018, Accepted 27 March 2018, Available online 27 March 2018, Version of Record 30 March 2018.
论文官网地址：https://doi.org/10.1016/j.patcog.2018.03.025
