Case of Culture: Triangulation-based Singer Identification for Duet Music Data Indexing

Authors: Wei-Ho Tsai ; Dept. of Electron. Eng., Nat. Taipei Univ. of Technol., Taipei, Taiwan ;   Cin-Hao Ma

Abstract: This study proposes a system to automatically identify multiple singers in a long audio stream that may have singing voices overlapping in time. The system is of great help in handling the rapid proliferation of music data. To achieve this, an audio stream is segmented into a sequence of consecutive, non-overlapping, fixed-length clips using a sliding window, and then undergoes solo/duet recognition, single singer identification, and duet singer identification. Furthermore, we propose a triangulation-based decision approach to improve the performance of singer identification. The results of the experiment conducted using a database of solo and duet a cappella demonstrate feasibility of the proposed system.

Published in: Big Data (BigData Congress), 2014 IEEE International Congress on

Date of Conference: June 27 2014-July 2 2014