Automatic music mood classification
With high popularity of audio files and increasing size of data storages devices, organizing audio files became a huge problem. In order to provide a better solution of storing and classifying music, this project will create a program that will automatically classify the music according to the mood...
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| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
2009
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| Online Access: | http://hdl.handle.net/10356/17931 |
| _version_ | 1811691343088451584 |
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| author | Tan, Kian Tong. |
| author2 | Wan Chunru |
| author_facet | Wan Chunru Tan, Kian Tong. |
| author_sort | Tan, Kian Tong. |
| collection | NTU |
| description | With high popularity of audio files and increasing size of data storages devices, organizing audio files became a huge problem. In order to provide a better solution of storing and classifying music, this project will create a program that will automatically classify the music according to the mood that is usually perceived by the listeners.
Before starting on how to classify music moods, one would need to know what the music moods are and how are they is going to be classified. It is found that Hevner’s classification of music moods is one of the ways that can classify the music moods. According to Hevner’s classification, there are eight major music mood categories. All the music emotions are classified under these eight groups.
In this project, statistical approach is being used towards automatic classification of music moods. As a result, data about the song needed to be gathered. This is being done by using MATLAB and MIR toolbox to help in extracting the music features and the data. Data being mainly mean and standard deviation of each music feature. A total 76 different data feature are obtained.
The song database is divided in to two, 320 training files and 160 testing files. In the end, only 71.25% of accuracy is obtained in classifying 4 groups. |
| first_indexed | 2024-10-01T06:18:23Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/17931 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:18:23Z |
| publishDate | 2009 |
| record_format | dspace |
| spelling | ntu-10356/179312023-07-07T15:47:10Z Automatic music mood classification Tan, Kian Tong. Wan Chunru School of Electrical and Electronic Engineering DRNTU::Engineering With high popularity of audio files and increasing size of data storages devices, organizing audio files became a huge problem. In order to provide a better solution of storing and classifying music, this project will create a program that will automatically classify the music according to the mood that is usually perceived by the listeners. Before starting on how to classify music moods, one would need to know what the music moods are and how are they is going to be classified. It is found that Hevner’s classification of music moods is one of the ways that can classify the music moods. According to Hevner’s classification, there are eight major music mood categories. All the music emotions are classified under these eight groups. In this project, statistical approach is being used towards automatic classification of music moods. As a result, data about the song needed to be gathered. This is being done by using MATLAB and MIR toolbox to help in extracting the music features and the data. Data being mainly mean and standard deviation of each music feature. A total 76 different data feature are obtained. The song database is divided in to two, 320 training files and 160 testing files. In the end, only 71.25% of accuracy is obtained in classifying 4 groups. Bachelor of Engineering 2009-06-18T02:11:19Z 2009-06-18T02:11:19Z 2009 2009 Final Year Project (FYP) http://hdl.handle.net/10356/17931 en Nanyang Technological University 85 p. application/pdf |
| spellingShingle | DRNTU::Engineering Tan, Kian Tong. Automatic music mood classification |
| title | Automatic music mood classification |
| title_full | Automatic music mood classification |
| title_fullStr | Automatic music mood classification |
| title_full_unstemmed | Automatic music mood classification |
| title_short | Automatic music mood classification |
| title_sort | automatic music mood classification |
| topic | DRNTU::Engineering |
| url | http://hdl.handle.net/10356/17931 |
| work_keys_str_mv | AT tankiantong automaticmusicmoodclassification |