基于机器学习的声源定位研究
发布时间：2019-04-10 11:42
【摘要】：当前,基于麦克风阵列信号处理的声源定位技术广泛应用于各种领域,如视频会议、语音增强、智能机器人、智能家居等。然而由于各种干扰,会使得声源定位性能降低,甚至无法定位,特别是室内环境下,常有混响、噪声等不利因素。因此,对于声源定位来说,如何能够提高恶劣条件下的鲁棒能力,提升定位准确性是一个研究重点。近年来,基于机器学习算法利用分类识别来进行声源定位开始得到关注,这类方法比起传统声源定位算法不仅有更强的鲁棒性,而且能够在麦克风无法收到直达声时依旧有效。本文基于机器学习算法研究如何在混响和噪声环境下更好地提升室内声源定位的性能。首先分析了声波传播模型和麦克风阵列信号接收模型,介绍了传统的GCC声源定位算法和SRP-PHAT声源定位算法,然后简要总结了机器学习算法。在此基础上,本文使用相位变换加权广义互相关函数作为特征,提出直接使用线性判别分析分类器去识别,仿真结果表明其定位性能在混响严重的情况下优于朴素贝叶斯分类器。接着利用线性判别分析对互相关函数进行特征变换,对投影后的特征使用分类识别的方式定位,在恶劣环境下其定位性能要大大强于未变换前。然后从单一分类器的研究推广到多个分类器的组合,使用Adaboost和Bagging方法对多个分类器集成,集成后定位性能比单一分类器更好。最后利用优化的Bagging方法进行声源定位,利用K均值聚类方法选择性集成个体分类器,进一步提高声源定位的鲁棒能力。
[Abstract]:At present, sound source localization technology based on microphone array signal processing is widely used in various fields, such as video conference, speech enhancement, intelligent robot, smart home and so on. However, due to a variety of interference, the performance of sound source location will be reduced, even unable to locate, especially in the indoor environment, there are often adverse factors such as reverberation, noise and so on. Therefore, how to improve the robust ability and improve the accuracy of sound source location under harsh conditions is the focus of research. In recent years, the machine learning algorithm based on classification identification for sound source localization has been paid more attention, this method is not only more robust than the traditional sound source localization algorithm, but also effective when the microphone can not receive direct sound. This paper studies how to improve the performance of indoor sound source location in reverberation and noise environment based on machine learning algorithm. Firstly, the acoustic propagation model and the microphone array signal receiving model are analyzed. The traditional GCC sound source localization algorithm and the SRP-PHAT sound source localization algorithm are introduced. Then the machine learning algorithm is briefly summarized. On this basis, this paper uses the phase transformation weighted generalized cross-correlation function as a feature, and proposes a linear discriminant analysis classifier to identify directly. The simulation results show that the localization performance is better than the naive Bayesian classifier in the case of severe reverberation. Then the linear discriminant analysis is used to transform the cross-correlation function and the projected feature is located by classification and recognition. The localization performance of the projected feature is much better than that before the transformation in bad environment. Then from the study of a single classifier to the combination of multiple classifiers, Adaboost and Bagging methods are used to integrate multiple classifiers, and the performance of the integrated classifier is better than that of a single classifier. Finally, the optimized Bagging method is used to locate the sound source, and the K-means clustering method is used to selectively integrate individual classifiers to further improve the robust ability of sound source location.
【学位授予单位】：南京邮电大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN912.3;TP181
本文编号：2455768
[Abstract]:At present, sound source localization technology based on microphone array signal processing is widely used in various fields, such as video conference, speech enhancement, intelligent robot, smart home and so on. However, due to a variety of interference, the performance of sound source location will be reduced, even unable to locate, especially in the indoor environment, there are often adverse factors such as reverberation, noise and so on. Therefore, how to improve the robust ability and improve the accuracy of sound source location under harsh conditions is the focus of research. In recent years, the machine learning algorithm based on classification identification for sound source localization has been paid more attention, this method is not only more robust than the traditional sound source localization algorithm, but also effective when the microphone can not receive direct sound. This paper studies how to improve the performance of indoor sound source location in reverberation and noise environment based on machine learning algorithm. Firstly, the acoustic propagation model and the microphone array signal receiving model are analyzed. The traditional GCC sound source localization algorithm and the SRP-PHAT sound source localization algorithm are introduced. Then the machine learning algorithm is briefly summarized. On this basis, this paper uses the phase transformation weighted generalized cross-correlation function as a feature, and proposes a linear discriminant analysis classifier to identify directly. The simulation results show that the localization performance is better than the naive Bayesian classifier in the case of severe reverberation. Then the linear discriminant analysis is used to transform the cross-correlation function and the projected feature is located by classification and recognition. The localization performance of the projected feature is much better than that before the transformation in bad environment. Then from the study of a single classifier to the combination of multiple classifiers, Adaboost and Bagging methods are used to integrate multiple classifiers, and the performance of the integrated classifier is better than that of a single classifier. Finally, the optimized Bagging method is used to locate the sound source, and the K-means clustering method is used to selectively integrate individual classifiers to further improve the robust ability of sound source location.
【学位授予单位】：南京邮电大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN912.3;TP181
【参考文献】
相关期刊论文 前1条
1 万新旺;吴镇扬;;基于双耳互相关函数的声源定位算法[J];东南大学学报(自然科学版);2011年05期
，本文编号：2455768
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