Microphone array-based sound source localization(SSL)is widely used in a variety of occasions such as video conferencing,robotic hearing,speech enhancement,speech recognition and so on.The traditional SSL methods cann...Microphone array-based sound source localization(SSL)is widely used in a variety of occasions such as video conferencing,robotic hearing,speech enhancement,speech recognition and so on.The traditional SSL methods cannot achieve satisfactory performance in adverse noisy and reverberant environments.In order to improve localization performance,a novel SSL algorithm using convolutional residual network(CRN)is proposed in this paper.The spatial features including time difference of arrivals(TDOAs)between microphone pairs and steered response power-phase transform(SRPPHAT)spatial spectrum are extracted in each Gammatone sub-band.The spatial features of different sub-bands with a frame are combine into a feature matrix as the input of CRN.The proposed algorithm employ CRN to fuse the spatial features.Since the CRN introduces the residual structure on the basis of the convolutional network,it reduce the difficulty of training procedure and accelerate the convergence of the model.A CRN model is learned from the training data in various reverberation and noise environments to establish the mapping regularity between the input feature and the sound azimuth.Through simulation verification,compared with the methods using traditional deep neural network,the proposed algorithm can achieve a better localization performance in SSL task,and provide better generalization capacity to untrained noise and reverberation.展开更多
Test tools and methods for synchronizing acoustic measurements in the course of stress-strain for seafloor sediment are elaborated and the test data of 45 sediment samples from the seafloor in the South China Sea are ...Test tools and methods for synchronizing acoustic measurements in the course of stress-strain for seafloor sediment are elaborated and the test data of 45 sediment samples from the seafloor in the South China Sea are analysed. The result shows that the coarser the sediment grains are, the smaller the porosity is and the larger the unconfined compression strength is, the higher the sound velocity is. In the course of stress-strain, the sediment sound velocity varies obviously with the stress. Acoustic characteristics of sediment in different strain phases and the influence of sediment microstructure change on its sound velocity are discussed. This study will be of important significance for surveying wells of petroleum geology and evaluating the base stabilization of seafloor engineering.展开更多
本文利用发音生理和声学语音材料讨论吴语宁波方言和苏州方言的前高元音的区别特征。文章发现宁波话3个前高元音[i y Y]拥有相似的舌位,其区别主要来自于唇型不同,其中[i]为展唇、[y]为水平撮唇、[Y]为垂直撮唇;苏州话4个前高元音[i y I...本文利用发音生理和声学语音材料讨论吴语宁波方言和苏州方言的前高元音的区别特征。文章发现宁波话3个前高元音[i y Y]拥有相似的舌位,其区别主要来自于唇型不同,其中[i]为展唇、[y]为水平撮唇、[Y]为垂直撮唇;苏州话4个前高元音[i y I Y]之间音位对立的区别特征则是[擦音性],苏州[I Y]在声学语音上可与一般语言中的前高元音[i y]类比,而苏州[i y]则是带有强摩擦的元音。结合语音学分析与历史演变脉络,文章认为两地前高元音之间这种强标记性的音位对立格局的形成来源于高元音继续高化这一历史音变。展开更多
基金supported by Nature Science Research Project of Higher Education Institutions in Jiangsu Province under Grant No.21KJB510018National Nature Science Foundation of China (NSFC)under Grant No.62001215.
文摘Microphone array-based sound source localization(SSL)is widely used in a variety of occasions such as video conferencing,robotic hearing,speech enhancement,speech recognition and so on.The traditional SSL methods cannot achieve satisfactory performance in adverse noisy and reverberant environments.In order to improve localization performance,a novel SSL algorithm using convolutional residual network(CRN)is proposed in this paper.The spatial features including time difference of arrivals(TDOAs)between microphone pairs and steered response power-phase transform(SRPPHAT)spatial spectrum are extracted in each Gammatone sub-band.The spatial features of different sub-bands with a frame are combine into a feature matrix as the input of CRN.The proposed algorithm employ CRN to fuse the spatial features.Since the CRN introduces the residual structure on the basis of the convolutional network,it reduce the difficulty of training procedure and accelerate the convergence of the model.A CRN model is learned from the training data in various reverberation and noise environments to establish the mapping regularity between the input feature and the sound azimuth.Through simulation verification,compared with the methods using traditional deep neural network,the proposed algorithm can achieve a better localization performance in SSL task,and provide better generalization capacity to untrained noise and reverberation.
基金funded by the Key Laboratory of Marginal Sea Geology, South China Sea Institute of Oceanology, Chinese Academy of Sciences (No. MSGL0606)the China National Natural Science Fundation (Ratification No. 40876018, 40476020)
文摘Test tools and methods for synchronizing acoustic measurements in the course of stress-strain for seafloor sediment are elaborated and the test data of 45 sediment samples from the seafloor in the South China Sea are analysed. The result shows that the coarser the sediment grains are, the smaller the porosity is and the larger the unconfined compression strength is, the higher the sound velocity is. In the course of stress-strain, the sediment sound velocity varies obviously with the stress. Acoustic characteristics of sediment in different strain phases and the influence of sediment microstructure change on its sound velocity are discussed. This study will be of important significance for surveying wells of petroleum geology and evaluating the base stabilization of seafloor engineering.
文摘本文利用发音生理和声学语音材料讨论吴语宁波方言和苏州方言的前高元音的区别特征。文章发现宁波话3个前高元音[i y Y]拥有相似的舌位,其区别主要来自于唇型不同,其中[i]为展唇、[y]为水平撮唇、[Y]为垂直撮唇;苏州话4个前高元音[i y I Y]之间音位对立的区别特征则是[擦音性],苏州[I Y]在声学语音上可与一般语言中的前高元音[i y]类比,而苏州[i y]则是带有强摩擦的元音。结合语音学分析与历史演变脉络,文章认为两地前高元音之间这种强标记性的音位对立格局的形成来源于高元音继续高化这一历史音变。
