An SOM Based Associative Memory Model For Memorizing Music Information
Date Issued
2005
Date
2005
Author(s)
Hsieh, Chen-Wei
DOI
en-US
Abstract
In the field of computer music, people have tried many methods to let computers recognize and understand music. For examples, Programs for recognizing pitch, tone, chord, and genre of music have been developed. Most of such music recognition or music-listening systems, people used statistics or digital signal processing to solve this kind of problem. Although these methods are powerful and useful, they have their own limits. In this thesis we try to simulate human memory for recognizing music. We construct an associative memory system, and use the association to recognize music.
Our associative memory system is based on neural network. Associative memory is not a new topic in neural network research. There have been many papers about associative memory since 1989. The major categories of associative memory include Hopfield model, Hopfield-like model, mind-in-a-box, and SOM based associative memory. Here we use SOM as a supervised sequential learning system for memorizing music information. With this system, the input midi music data passes the pre-processing and the training process, to generate memory. The recall will be conducted by pieces of music.
Subjects
關聯式記憶
自我組織圖
音樂
記憶
SOM
Associative Memory
Music
Type
thesis
File(s)![Thumbnail Image]()
Loading...
Name
ntu-94-R92921094-1.pdf
Size
23.31 KB
Format
Adobe PDF
Checksum
(MD5):18875ec728653e20b79fe82d2efe89e9