面向多声源的压缩感知麦克风阵列的波达方向估计

在语音识别、说话人识别等语音交互应用领域中,麦克风阵列常常工作于多声源工作场景,因而需要更高的波达方向(DOA)估计分辨性能.压缩感知(CS)的DOA估计算法可将声源定位的问题转化成稀疏信号的重构问题,进而提高在高混响、低信噪比环境下的DOA估计性能.基于这一思想,将CS方法应用于多声源方位估计.考虑到传统的基于CS的DOA估计算法利用实测声源传输响应作为混合矩阵时,会因噪声的存在而导致多声源条件下的匹配程度下降,提出了利用基于阵列各阵元之间时延关系所生成的不同方位的声源传输响应来构造CS混合矩阵,即构造房间冲激响应CS(CRR-CS)的DOA估计算法,从而实现多声源的DOA稀疏恢复.通过实验验证了该方法优于传统方法,能更好地实现定位.福建省高校产学合作项目(2015H6019);;福建省中青年教师教育科研项目(JAS170012

采用多通道信道均衡的调频水声语音通信

与数字语音通信体制相比,调频语音通信技术具有易于实现、抗干扰能力强等优点。在低信噪比、严重多径干扰条件下调频语音通信性能受到较大影响。针对蛙人、潜器上行语音通信需求,采用在正交解调的调频语音通信系统中引入多通道均衡处理抑制码间干扰和信道衰落的影响,同时基于客观语音质量评估方法进行调频水声语音通信质量评估。海试结果验证了该方案的有效性。国家自然科学基金项目(11574258

利用块稀疏特性的压缩感知麦克风阵列声源定位

与传统声源定位算法如相位变换加权、时延累加定位不同,压缩感知麦克风阵列声源定位算法可将声源定位转化为稀疏重构问题从而获得较高的性能。但在实际应用环境下,由于远场声源自身指向性、空间混响等原因,声源方向向量往往呈现块稀疏度结构,导致采用传统稀疏恢复算法如正交匹配追踪算法(Orthogonal matching pursuit,OMP)等进行压缩感知定位性能下降。本文在压缩感知声源定位算法中引入块稀疏似零范数,以压缩感知为基本框架,采用块稀疏似零范数稀疏恢复进行声源方向向量的重构,获取声源的方位。实验结果表明,相较于传统声源定位算法和基于OMP的压缩感知声源定位算法,本文算法具有更高的定位精度。国家自然科学基金（11574258）资助项目;;\n福建省高校产学合作（2015H6019）资助项目;;\n重点实验室基金一般项目（6142109180303）资助项

A Microphone-array De-reverberation Method Using Sidelobe Enhancement

在音频/视频会议、人机交互、语音识别等领域,严重的混响干扰导致麦克风阵列语音处理性能急剧下降.针对现有逆滤波等抗混响方法需要获得准确的房间传输响; 应.而波束形成方法抗混响性能有限的问题,基于广义旁瓣抵消器(generalized sidelobe; canceller,GSC)结构提出一种采用旁瓣增强的麦克风阵列抗混响算法(Sidelobeenhanceing reverberation; mitigation algorithm for microphone; array,SERM).该算法首先进行波束形成,初步获得增强的直达语音信号,并对旁瓣获取的混响分量进行自适应迭代增强,再将旁瓣迭代增强的误差信号; 作为参考噪声进行自适应噪声抵消.最终输出抗混响语音.实验结果表明,在混响环境下该方法能有效改善麦克风阵列的语音信号质量.In many practical applications such as audio/ video; conferences,man-machine interfaces,speech recognitions, the; reverberation leads to significant degradation of microphone array; speech processing.However,while the conventional de-reverberation; methods, such as inverse-filtering method,need to obtain accurate room; transmission responses,the classic beam-forming method only yields; limited performance.Based on the Generalized Sidelobe Canceller (GSC); structure,this paper proposes a novel microphone-array de-reverberation; method to achieve sidelobe exploitation. First, we use the contemporary; beam-forming method to initially obtain the direct speech signals.Then; we adopt the adaptive iterative algorithm to explore reverberation; components in sidelobes for further enhancements. Finally, the error of; the sidelobe enhancement is adopted as the reference noise of adaptive; noise cancellation to achieve the de-reverberation. Experimental results; show that the proposed method effectively improves the quality of speech; signal under the reverberation environment.福建省高校产学合作项目; 福建省中青年教师教育科研项目; 厦门市科技计划项

Determination of the number of ψ(3686) events taken at BESIII

The number of ψ(3686) events collected by the BESIII detector during the 2021 run period is determined to be (2259.3±11.1)×106 by counting inclusive ψ(3686) hadronic events. The uncertainty is systematic and the statistical uncertainty is negligible. Meanwhile, the numbers of ψ(3686) events collected during the 2009 and 2012 run periods are updated to be (107.7±0.6)×106 and (345.4±2.6)×106, respectively. Both numbers are consistent with the previous measurements within one standard deviation. The total number of ψ(3686) events in the three data samples is (2712.4±14.3)×10^