Multiple Source Segmentation and Separation Using MUSIC Algorithm with Calibrated Array Manifold Vector

• Main Authors: Lu, Meng-Wei, 呂孟瑋
• Other Authors: Hu, Jwu-Sheng
• Format: Others
• Language: zh-TW
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/74719746288874524207

碩士 === 國立交通大學 === 工學院聲音與音樂創意科技碩士學位學程 === 102 === This thesis proposes a system structure for multiple sound sources segmentation and separation using MUSIC (Multiple Signal Classification) algorithm. Using a calibrated array manifold vector, the proposed calibration method improves the accuracy of the MUSIC algorithm for wide-band detections, hence providing high accuracy source segmentation and separation results. The system structure uses a multiple signal classification algorithm to detect the location of sound sources and estimate their spectrum distributions. The multiple sources tracking method is implemented by a probability decision method regarding spatial and spectrum distributions. Using the estimated directivity, multiple sources were extracted from the array signals using beamforming methods. This proposed system structure can track and separate multiple sources at the same time and maintain high detection rate under very low SNR conditions.