2017 IEEE 14th International Conference on e-Business Engineering (ICEBE) (2017)
Nov. 4, 2017 to Nov. 6, 2017
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICEBE.2017.51
Music Recognition System has becoming popular these days. They are based on either audio or symbolic method, which compares the user's query with the existing music database. The investigation has shown that the audio-based method system is good for storing sound waves. However, the limitation is to illustrate the content of the music. The symbolic-based method system doing well in representing the content of the music, including recognize similar patterns, but limitation in creating the music, e.g., Electronic Music. We also carried some detailed tests based on the previous Neural Network systems, with Music Definition Language (MDL) and Music Manipulation Language (MML). Furthermore, we have tested our previous classification system with new melody query, to see how it can handle with external music pieces, which beyond the self-testing from a self-organising-map. The conclusion is that our system can classify variation type, including key variations, expansion and reduction, which is better than those existing Music Information Retrieval (MIR) systems.
audio databases, information retrieval, information retrieval systems, music, self-organising feature maps
H. Li, Z. Tang, X. Fei, K. Chao, M. Yang and C. He, "A Survey of Audio MIR Systems, Symbolic MIR Systems and a Music Definition Language Demo-System," 2017 IEEE 14th International Conference on e-Business Engineering (ICEBE), Shanghai, China, 2018, pp. 275-281.