{"title":"基于小尺寸麦克风阵列的服务机器人大规模三维声源定位方法","authors":"Long Chen, Lei Huang, Guitong Chen, Weize Sun","doi":"10.1109/ICICSP55539.2022.10050648","DOIUrl":null,"url":null,"abstract":"Ahstract-The working frequency range and the scale of the scanning area of a microphone array are typically limited by the array geometry. Owing to its movable feature, for the service robots, achieving a wider working frequency range with a 3-dimension global view requires a virtually larger and denser 3-dimension array, which can be realised by using non-synchronous measurements beamforming with a movable microphone prototype array. However, even when using the state-of-the-art method, it is challenging to localise multiple broadband sources, owing to the difficulty in selecting an appropriate operating frequency without any prior information about the target signal. Therefore, this paper proposes a tensor-completion-based non-synchronous measurement method for broadband multiple-sound-source localisation. The tensor data structure of the broadband signal is analysed, and an alternating direction method based on multiplier optimisation with a tensor multi-norm constraint is proposed. This algorithm can provide a sound map with a distinct 3-dimension global view of different speech signal sources with high accuracy via a 16-channel planar microphone array. Compared with the matrix-based optimisation method, the proposed method can significantly reduce the mean square error of the estimated source location.","PeriodicalId":281095,"journal":{"name":"2022 5th International Conference on Information Communication and Signal Processing (ICICSP)","volume":"133 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Large Scale 3D Sound Source Localisation Approach Achieved via Small Size Microphone Array for Service Robots\",\"authors\":\"Long Chen, Lei Huang, Guitong Chen, Weize Sun\",\"doi\":\"10.1109/ICICSP55539.2022.10050648\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Ahstract-The working frequency range and the scale of the scanning area of a microphone array are typically limited by the array geometry. Owing to its movable feature, for the service robots, achieving a wider working frequency range with a 3-dimension global view requires a virtually larger and denser 3-dimension array, which can be realised by using non-synchronous measurements beamforming with a movable microphone prototype array. However, even when using the state-of-the-art method, it is challenging to localise multiple broadband sources, owing to the difficulty in selecting an appropriate operating frequency without any prior information about the target signal. Therefore, this paper proposes a tensor-completion-based non-synchronous measurement method for broadband multiple-sound-source localisation. The tensor data structure of the broadband signal is analysed, and an alternating direction method based on multiplier optimisation with a tensor multi-norm constraint is proposed. This algorithm can provide a sound map with a distinct 3-dimension global view of different speech signal sources with high accuracy via a 16-channel planar microphone array. Compared with the matrix-based optimisation method, the proposed method can significantly reduce the mean square error of the estimated source location.\",\"PeriodicalId\":281095,\"journal\":{\"name\":\"2022 5th International Conference on Information Communication and Signal Processing (ICICSP)\",\"volume\":\"133 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-11-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 5th International Conference on Information Communication and Signal Processing (ICICSP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICICSP55539.2022.10050648\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 5th International Conference on Information Communication and Signal Processing (ICICSP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICICSP55539.2022.10050648","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Large Scale 3D Sound Source Localisation Approach Achieved via Small Size Microphone Array for Service Robots
Ahstract-The working frequency range and the scale of the scanning area of a microphone array are typically limited by the array geometry. Owing to its movable feature, for the service robots, achieving a wider working frequency range with a 3-dimension global view requires a virtually larger and denser 3-dimension array, which can be realised by using non-synchronous measurements beamforming with a movable microphone prototype array. However, even when using the state-of-the-art method, it is challenging to localise multiple broadband sources, owing to the difficulty in selecting an appropriate operating frequency without any prior information about the target signal. Therefore, this paper proposes a tensor-completion-based non-synchronous measurement method for broadband multiple-sound-source localisation. The tensor data structure of the broadband signal is analysed, and an alternating direction method based on multiplier optimisation with a tensor multi-norm constraint is proposed. This algorithm can provide a sound map with a distinct 3-dimension global view of different speech signal sources with high accuracy via a 16-channel planar microphone array. Compared with the matrix-based optimisation method, the proposed method can significantly reduce the mean square error of the estimated source location.