基于LeNet-RES的室内声源区域定位算法

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关键词：室内声源定位；麦克风阵列；神经网络；相位变换加权广义互相关函数；信号处理；房间脉冲响应中图分类号：TN912-34；TP183 文献标识码：A 文章编号：1004-373X（2025）10-0020-05

Abstract：Undertheconditionsoflowsignal-to-noiseratio（SNR）andhighreverberation，indorsoundsourceregional localizationbecomesmorechalenging.Inordertosolvethisproblem，aneuralnetworkLeNet-RESisdesigned，whichuses residualblockstoimproveLeNet，therebyimprovingtheperformanceofthenetwork.Thedatasetforindoorsoundsourceis obtainedbysimulating theroomimpulseresponseofan8-araycuboid microphonearay.Thesignalreceivedbythemicrophone is processed intoframes，ndthegeneralizedcrosscorelationPHAse transformation （GCC-PHAT）betweeneachframesignalsis calculated.Thisfunctionisarrngedintotwo-dimensionaldataasinputfeatures.Thefialnetworkmodelistrainedbytakigthe rompartitionarealabelasthenetworkoutput.Intheexperiment，thepositioningaccracyofthetwoneuralnetworkswas tested whenthenumberofroompartitionswas8and16，respectively.Theresultsshow thatunder thesameSNRconditions，the accuracyofLeNet-RES-16is 81.33% when thenumber of room partitionsis16 and the reverberation time is O.6s，which is 23% higherthanthatofLeNet-16;underthesamereverberationconditions，theaccuracyofLeNet-RES-16is84.16%whenthe numberofpartionsis16andtheSNRisO，whichis29%higherthanthatofLeNet-16.Theregionallocalizationperformanceof LeNet-RES is better than that of LeNet under various SNR and various reverberation times.

Keywords：indoorsound sourcelocalization；microphonearray；neural network；generalizedcrosscorelation PHAse transformation; signal processing;room impulse response

0 引言

传统室内声源定位采用麦克风阵列信号处理算法来实现，被广泛应用于智能家居、安防监控、大型会议、车载系统等多个领域[1-5]。（剩余7727字）