Classification of music based on correlation between mood, linguistic and audio features
This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018.
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BRAC University
2018
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| Rochtain ar líne: | http://hdl.handle.net/10361/10905 |
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10361-109052022-01-26T10:18:14Z Classification of music based on correlation between mood, linguistic and audio features Sobhan, Md. Mashrur Bari Zaber, Moinul Department of Computer Science and Engineering, BRAC University Music Linguistic Audio features Classification -- Music. This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018. Cataloged from PDF version of thesis. Includes bibliographical references (pages 35). The emergence of music in recent times has been enviable. Some people consider music to be an integral part of their regular lives, while others sometimes even consider music to be some divine inspiration setting the mood for them for the rest of the day. For such people, a well-trimmed precise playlist of the songs that they would love to listen to, based on genre or mood of the songs, is priceless. Genre of an individual song is very much available, as that information is mostly provided within the song, but getting to judge the mood of the song is much more of a challenge. If it is a challenge itself for one distinct song, then one can easily imagine the hassle that a person faces when selecting a playlist of songs from a huge library of music. This ultimately gives rise to the importance of the classification of music based on the mood of the individual songs. This project establishes such a method, which ultimately works with a combination of features, such as the linguistic and audio features of a song to classify a song according to the mood the song represents or is appropriate for. These features are then used in conjunction with several metrics to find out their relevance or relationships and measured for validation purposes. Md. Mashrur Bari Sobhan B. Computer Science and Engineering 2018-11-29T06:20:49Z 2018-11-29T06:20:49Z 2018 2018-05 Thesis ID 16373015 http://hdl.handle.net/10361/10905 en BRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 35 pages application/pdf BRAC University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Music Linguistic Audio features Classification -- Music. |
| spellingShingle |
Music Linguistic Audio features Classification -- Music. Sobhan, Md. Mashrur Bari Classification of music based on correlation between mood, linguistic and audio features |
| description |
This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018. |
| author2 |
Zaber, Moinul |
| author_facet |
Zaber, Moinul Sobhan, Md. Mashrur Bari |
| format |
Thesis |
| author |
Sobhan, Md. Mashrur Bari |
| author_sort |
Sobhan, Md. Mashrur Bari |
| title |
Classification of music based on correlation between mood, linguistic and audio features |
| title_short |
Classification of music based on correlation between mood, linguistic and audio features |
| title_full |
Classification of music based on correlation between mood, linguistic and audio features |
| title_fullStr |
Classification of music based on correlation between mood, linguistic and audio features |
| title_full_unstemmed |
Classification of music based on correlation between mood, linguistic and audio features |
| title_sort |
classification of music based on correlation between mood, linguistic and audio features |
| publisher |
BRAC University |
| publishDate |
2018 |
| url |
http://hdl.handle.net/10361/10905 |
| work_keys_str_mv |
AT sobhanmdmashrurbari classificationofmusicbasedoncorrelationbetweenmoodlinguisticandaudiofeatures |
| _version_ |
1814308674883026944 |