基于压缩感知的双麦克风混响多声源定位算法
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摘要
针对混响条件下现有声源定位技术中麦克风数量必须大于声源数量的现状,提出了一种基于压缩感知的双麦克风混响多声源(至少3个声源)定位算法。将多声源定位问题看作是块稀疏信号的重构问题,在频域将全房间冲激响应归一化来构造压缩观测矩阵,重构的块稀疏信号中非零块的位置即对应了空间中实际声源的位置。仿真实验表明,与基于子带可控响应功率(SRP-sub)的多声源定位方法相比,在双麦克风混响条件下定位多声源,基于压缩感知的多声源定位算法的定位性能更高,在混响时间为0.6 s时,仅采用40个频点值,定位3个声源的成功率可以达到80%。
In traditional multi-source localization field,it is necessary to guarantee that the number of microphone is more than the number of source.To overcome this constraint,a dual-microphone multi-source localization algorithm based on CS was proposed,where the number of sound source localized successfully was more than 3.The multi-source localization was regarded as the block sparse signal reconstruction in this algorithm,and the full room impulse responses normalized were exploited to construct the compressed observation matrix in frequency domain.In reconstructed block sparse signal,the positions of non-zero blocks were corresponded to the positions of sound sources in space.The simulation shows that compared with the SRP-sub algorithm,in reverberation time 0.6s with dual-microphone,the proposed multi-source localization algorithm based on compressed sensing has higher capability which can reach 80% success rate by using 40 frequency points to localize 3 sound sources.
In traditional multi-source localization field,it is necessary to guarantee that the number of microphone is more than the number of source.To overcome this constraint,a dual-microphone multi-source localization algorithm based on CS was proposed,where the number of sound source localized successfully was more than 3.The multi-source localization was regarded as the block sparse signal reconstruction in this algorithm,and the full room impulse responses normalized were exploited to construct the compressed observation matrix in frequency domain.In reconstructed block sparse signal,the positions of non-zero blocks were corresponded to the positions of sound sources in space.The simulation shows that compared with the SRP-sub algorithm,in reverberation time 0.6s with dual-microphone,the proposed multi-source localization algorithm based on compressed sensing has higher capability which can reach 80% success rate by using 40 frequency points to localize 3 sound sources.
引文
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[21]ASAEI A,GOLBABAEE M,BOURLARD H,et al.Structured Sparsity models for reverberant speech separation[J].IEEE/ACM Transactions on Audio Speech&Language Processing,2014,22(3):620-633.
[22]ALLEN J B,BERKLEY D A.Image method for efficiently simulating small-room acoustics[J].Journal of the Acoustical Society of America,1979,65(S1):943-950.
[23]ASAEI A,BOURLARD H,TAGHIZADEH M J,et al.Model-based sparse component analysis for reverberant speech localization[C]//2014 IEEE International Conference on Acoustics,Speech and Signal Processing 2014:1439-1443.
[24]MAJUMDAR A.Greedy algorithms promoting group sparsity[Z].Math Works,2009.
[25]NEEDELL D,VERSHYNIN R.Uniform uncertainty principle and signal recovery via regularized orthogonal matching pursuit[J].Foundations of Computational Mathematics,2009,9(3):317-334.
[26]ELDAR Y C,KUPPINGER P,BOLCSKEI H.block-sparse signal:uncertainty relations and efficient recovery[J].IEEE Transactions on Signal Processing,2010,58(6):3042-3054.
[2]刘超.基于麦克风阵列的声源定位算法研究[D].南京:南京大学,2015.LIU C.Study of sound source localization algorithm based on microphone array[D].Nanjing:Nanjing University,2015.
[3]石婷.麦克风阵列声源定位算法研究综述[J].科技视界,2016(27):274.SHI T.Research on the algorithms of sound source localization based on microphone array[J].Science&Technology Vision,2016(27):274-274.
[4]赵圣.基于麦克风阵列的仿人智能机器人声源定位技术的研究[D].哈尔滨:哈尔滨工业大学,2013.ZHAO S.Research on sound source localization technology of humanoid intelligent robot based on microphone array[D].Harbin:Harbin Institute of Technology,2013.
[5]ASAEI A,BOURLARD H,CEVHER V.Model-based compressive sensing for multi-party distant speech recognition[C]//2011 IEEE International Conference on Acoustics,Speech and Signal Processing.2011:4600-4603.
[6]WABNITZ A,EPAIN N,SCHAIK A V,et al.Time domain reconstruction of spatial sound fields using compressed sensing[C]//2011IEEE International Conference on Acoustics,Speech and Signal Processing.2011:465-468.
[7]BENICHOUX A,VINCENT E,GRIBONVAL R.A compressed sensing approach to the simultaneous recording of multiple room impulse responses[C]//2011 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics.2011:285-288.
[8]MIGNOT R,CHARDON G,DAUDET L.Low frequency interpolation of room impulse responses using compressed sensing[J].IEEE/ACM Transactions on Audio,Speech and Language Processing,2014,22(1):205-216.
[9]CHARDON G.A block-sparse music algorithm for the localization and the identification of directive sources[C]//2014 IEEE International Conference on Acoustics,Speech and Signal Processing.2014:3953-3957.
[10]赵小燕,周琳,吴镇扬.基于压缩感知的麦克风阵列声源定位算法[J].东南大学学报(自然科学版),2015,45(2):203-207.ZHAO X Y,ZHOU L,WU Z Y.Compressed sensing-based sound source localization algorithm for microphone array[J].Journal of Southeast University(Natural Science Edition),2015,45(2):203-207.
[11]DO H,SILVERMAN H F.A method for locating multiple sources from a frame of a large-aperture microphone array data without tracking[C]//IEEE International Conference on Acoustics,Speech,and Signal Process.2008:301-304.
[12]倪志莲,蔡卫平,张怡典.基于子带可控响应功率的多声源定位方法[J].计算机工程与应用,2013,49(24):205-209.NI Z L,CAI W P,ZHANG Y D.Method for multiple speech source localization based on sub-band steered response power[J].Computer Engineering and Applications,2013,49(24):205-209.
[13]ASAEI A,BOURLARD H,TAGHIZADEH M J,et al.Computational methods for underdetermined convolutive speech localization and separation via model-based sparse component analysis[J].Speech Communication,2016,76(C):201-217.
[14]石光明,刘丹华,高大化,等.压缩感知理论及其研究进展[J].电子学报,2009,37(5):1070-1081.SHI G M,LIU D H,GAO D H,et al.Advances in theory and application of compressed sensing[J].Chinese Journal of Electronics,2009,37(5):1070-1081.
[15]ZHAO R,LIN W,LI H,et al.Reconstruction algorithm for compressive sensing based on smoothed???怺?norm and revised newton method[J].Journal of Computer-Aided Design&Computer Graphics,2012,24(4):478-484.
[16]李珅,马彩文,李艳,等.压缩感知重构算法综述[J].红外与激光工程,2013,42(s1):225-232.LI K,MA C W,LI Y,et al.Survey on reconstruction algorithm based on compressive sensing[J].Infrared and Laser Engineering,2013,42(s1):225-232.
[17]YAGHOOBI M,WU D,DAVIES M E.Fast non-negative orthogonal matching pursuit[J].IEEE Signal Processing Letters,2015,22(9):1229-1233.
[18]ZHOU Y,ZENG F Z,GU Y C.A gradient descent sparse adaptive matching pursuit algorithm based on compressive sensing[C]//2017IEEE International Conference on Machine Learning and Cybernetics.2017:2883-2890.
[19]ZHONG J,WEN G,MA C,et al.Radar signal reconstruction algorithm based on complex block sparse bayesian learning[C]//2015 IEEEInternational Conference on Signal Processing.2015:1930-1933.
[20]HAMARUNNISA P,SHERIKH K K.An efficient structured sparsity model for reverberant speech separation[J].International Journal of Advanced Trends in Computer Science&Engineering,2015,4(10):14658-14662.
[21]ASAEI A,GOLBABAEE M,BOURLARD H,et al.Structured Sparsity models for reverberant speech separation[J].IEEE/ACM Transactions on Audio Speech&Language Processing,2014,22(3):620-633.
[22]ALLEN J B,BERKLEY D A.Image method for efficiently simulating small-room acoustics[J].Journal of the Acoustical Society of America,1979,65(S1):943-950.
[23]ASAEI A,BOURLARD H,TAGHIZADEH M J,et al.Model-based sparse component analysis for reverberant speech localization[C]//2014 IEEE International Conference on Acoustics,Speech and Signal Processing 2014:1439-1443.
[24]MAJUMDAR A.Greedy algorithms promoting group sparsity[Z].Math Works,2009.
[25]NEEDELL D,VERSHYNIN R.Uniform uncertainty principle and signal recovery via regularized orthogonal matching pursuit[J].Foundations of Computational Mathematics,2009,9(3):317-334.
[26]ELDAR Y C,KUPPINGER P,BOLCSKEI H.block-sparse signal:uncertainty relations and efficient recovery[J].IEEE Transactions on Signal Processing,2010,58(6):3042-3054.