Music information retrieval system using complex-valued recurrent neural networks

Maki Kataoka, Makoto Kinouchi, Masafumi Hagiwara

Research output: Contribution to journalConference articlepeer-review

10 Citations (Scopus)


In this paper, we propose a music information retrieval system using complex-valued recurrent neural networks. Melodies can be treated as temporal sequences. The multilayer network using complex neurons with local feedback (MNCF) is used in the proposed system because of the high ability to deal with temporal sequences. The process of retrieval is as follows: First, the pitch and duration of each note are extracted from the key melody which is inputted from the MIDI keyboard. Second, they are inputted to some MNCFs. When the output values of each MNCF agree with the next input values for several times, it produces the database address. The user can listen to the candidate of the melody. In addition, the user can know the text information. The proposed system has the following other features: the user can input any parts of the melody he/she remembers as a key input; the human interface is excellent for users. The retrieval is performed in parallel because of the inherent parallelism of neural networks; the proposed system is robust for transposition and change of the tempo. Wxse have evaluated the proposed system by experiment. The system retrieved about 91.5% of melodies correctly.

Original languageEnglish
Pages (from-to)4290-4295
Number of pages6
JournalProceedings of the IEEE International Conference on Systems, Man and Cybernetics
Publication statusPublished - 1998 Dec 1
EventProceedings of the 1998 IEEE International Conference on Systems, Man, and Cybernetics. Part 3 (of 5) - San Diego, CA, USA
Duration: 1998 Oct 111998 Oct 14

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Hardware and Architecture


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