基于多状态跳转模型的场景独立音频事件检测方法

doi:10.7523/j.issn.2095-6134.2019.02.009

中国科学院大学学报 ›› 2019, Vol. 36 ›› Issue (2): 218-225.DOI: 10.7523/j.issn.2095-6134.2019.02.009

基于多状态跳转模型的场景独立音频事件检测方法

王健飞, 张卫强, 刘加

清华大学电子工程系, 北京 100084

收稿日期:2017-12-06 修回日期:2018-04-08 发布日期:2019-03-15
通讯作者: 张卫强
基金资助:
国家自然科学基金（U1836219）资助

Scene-independent sound event detection based on multi-state transition model

WANG Jianfei, ZHANG Weiqiang, LIU Jia

Department of Electronic Engineering, Tsinghua University, Beijing 100084, China

Received:2017-12-06 Revised:2018-04-08 Published:2019-03-15

摘要/Abstract

摘要： 针对不同类型事件设计多状态跳转模型，结合两种深度神经网络实现对传统音频事件检测框架的改进。实验表明，在DCASE2017任务2的开发集数据上，改进后的DNN-HMM系统相比于基线系统取得F值8.9%的相对提升和错误率19%的绝对下降；基于多状态跳转模型聚类的卷积神经网络模型（SC-CNN），相比于基线系统取得F值18%的相对提升和错误率30%的绝对下降。

关键词: 音频事件检测, 多状态跳转模型, 深度神经网络, 迁移学习, 多任务学习

Abstract: We designed the multi-state transition model for different types of sound events, and combined two kinds of deep neural network to achieve the improvement of the traditional framework. The performance evaluated on the DCASE2017 task2 development dataset showed that the improved DNN-HMM system outperformed the baseline and achieved 19% absolutely lower error rate (ER) and 8.9% relatively higher F-score. The state clustering convolutional neural network (SC-CNN) system based on multi-state transition model also achieved 18% relatively higher F-score and 30% absolutely lower ER, which has reached the international advanced level.

Key words: sound event detection, multi-state transition model, deep neural network, transfer learning, multitask learning

中图分类号:

TN912

王健飞, 张卫强, 刘加. 基于多状态跳转模型的场景独立音频事件检测方法[J]. 中国科学院大学学报, 2019, 36(2): 218-225.

WANG Jianfei, ZHANG Weiqiang, LIU Jia. Scene-independent sound event detection based on multi-state transition model[J]. , 2019, 36(2): 218-225.

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基于多状态跳转模型的场景独立音频事件检测方法

Scene-independent sound event detection based on multi-state transition model

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