Open AccessImproved similarity fusion scheme for cover song identification
Author(s) -
Fan Yanlan,
Chen Ning
Publication year - 2018
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2018.6461
Subject(s) - similarity (geometry) , pattern recognition (psychology) , fuse (electrical) , artificial intelligence , computer science , identification (biology) , cluster analysis , scheme (mathematics) , graph , fusion , mathematics , data mining , algorithm , theoretical computer science , engineering , mathematical analysis , linguistics , philosophy , botany , electrical engineering , image (mathematics) , biology
A Cover Song Identification (CSI) scheme based on non‐linear graph fusion and Tensor Product Graphs (TPGs) diffusion is proposed as an improvement to the authors' previously proposed similarity fusion‐based CSI scheme. First, the harmonic progression, melody evolution, and rhythm‐based descriptors are extracted from the track, respectively. Next, Similarity Network Fusion is adopted to fuse the similarity graphs obtained based on two types of descriptors to take full use of the common as well as complementary properties between them. Finally, TPGs diffusion is performed on the obtained fused similarity graphs to take advantage of the manifold structure contained in them to improve the performance, further. Experimental results demonstrate the superiority of the proposed scheme over their previously proposed one, in terms of identification accuracy and clustering performance.