四川大学学报（自然科学版）2024，Vol.61Issue(4)：225-231,7.DOI:10.19907/j.0490-6756.2024.043006
基于听觉融合特征的多声音事件检测
Multiple sound event detection based on auditory fusion features
摘要
Abstract
In order to improve the performance of multi-sound event detection task,this paper conducts an in-depth study of the Cascade of Asymmetric Resonators with Fast-Acting Compression(CARFAC)digital co-chlear model,and proposes a multi-sound event detection method based on auditory fusion features.Initially,the CARFAC is employed to extract the Neural Activity Pattern(NAP)of mixed sound.Subsequently,the NAP is concatenated with Gammatone Frequency Cepstral Coefficients(GFCC)to generate fused auditory features,which are then fed into a Convolutional Recurrent Neural Network(CRNN)for fully supervised learning to detect urban sound events.Experimental results demonstrate that,in the scenario of low signal-to-noise ratio and a higher number of overlapping events,the fused auditory features exhibit superior robustness and multi-sound event detection performance compared to individual features such as NAP,MFCC,and GFCC.关键词
数字耳蜗模型/神经活动模式/融合听觉特征/声音事件检测/四折交叉验证Key words
Digital cochlear model/Neural activity pattern/Fused auditory parameters/Sound event detec-tion/Four-fold cross validation分类
信息技术与安全科学引用本文复制引用
罗吉,夏秀渝..基于听觉融合特征的多声音事件检测[J].四川大学学报（自然科学版）,2024,61(4):225-231,7.基金项目
国家自然科学基金联合基金项目(U1733109) （U1733109）
