Using Neural Networks to classify Music

Research Proposal: Emotion-based Music Analysis
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Emotion-based Music Analysis

Introduction:

Hearing is an important resource of information, and it is a quite interesting question of how to make a computer hear as human beings. For the convenience of researching, people classify machine hearing into three domains: speech signal processing and recognition, common analysis of audio signal, and content-based music analysis. Speech signal processing and recognition has been a traditional research field for many years. There are many summarizing works about it. With the rapid progress of the information science and techniques, the content-based music analysis and general audio signal processing have gradually become hotspots of research in the fields of pattern recognition, and multimedia data processing.

There are several specific research areas in music signal analysis. For example, some people try to work on how to identify the type of instrument in a piece of audio.  Kermit and Eide have done research on how to automatically identify Guitar, Keyboard, and Drum [1]. They first used Haar Wavelet Transform to pre-treat the audio signal to get characters, and then an Annual Neural Network was trained to classify the test data. Josephson and Schneider have researched the effectiveness of fuzzy expert system on instrument identification in a reverberated situation [2]. They listed several practical examples and suggested to adopt quantitative physical analysis combining Fuzzy Expert system, which actually got ideal results in their research.

Source: yilingu23

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