Browsing by Author "Mimo, Mehejabin"

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    Applying text analytics on music emotion recognition
    (Daffodil International University, 2018-05-07) Hossain, Rafayet; Sarker, Md. Rahmatul kabir rasel; Mimo, Mehejabin
    Music is most important part in human life. When different kind of words are prepared a new sound which is enjoyable to the human beings, it is called music. Music is not just a source of entertainment. It is something more than entertainment. Title should come after the song is finished and should reproduce a termination of the lyrical content. In this paper our proposed system have recommend a proper song title based on song lyrics. We have applied topic model algorithm Latent Dirichlet allocation (LDA) for song title recommendation. In this paper another experiment is music emotion recognition. Song feel emotionally different to listeners depending on their lyrical contents. Emotions classify is so difficult through the existing music emotion classification method .We have extracted eight features from song lyrics. We propose a method for lyrics based emotion classification using feature selection. We also proposed another experiment music personality trait. We have to generate a customize dataset based on music interest and 20 questions of big five personality model. Our proposed module would be helpful for user. Song title recommendation system produces satisfactory result. We may use this module to recommend song title from lyrics. Music emotion recognition system will help to predict the overall emotional state of a user. Music personality traits could be useful to find out the personality measurements of any user.
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    Recommendation Approach of English Songs Title Based on Latent Dirichlet Allocation Applied on Lyrics
    (Proceedings of 2019 3rd IEEE International Conference on Electrical, Computer and Communication Technologies, ICECCT 2019, IEEE, 2019-10-17) Hossain, Rafayet; Sarker, Md. Rahmatul Kabir Rasel; Mimo, Mehejabin; Marouf, Ahmed Al; Pandey, Bishwajeet
    The significance of music has evolved due to the vast diversity of entertainment industry. Songs are the widely used entertainment segment that can influence directly to the heart of the listeners. Choosing a suitable title for a song is considered as a common problem faced by the music directors. As the title gives the first impression of the song and only by the title listeners usually decide whether they will listen to this song or not, thus makes it a challenging task to determine. Lyrics are the most influential part of a particular song apart from the tune, rhythm, fusion, singer, genre etc. In this paper, we propose an approach to estimate and recommend the title of the song based on its lyrics. We have applied Latent Dirichlet Allocation (LDA) to find the hidden or implied topic of the song. The output of the LDA algorithm provides scoring on the significant words, which are passed to an estimation process to generate a song title. The proposed approach was experimented on over 200 English songs database having vast diversity in genre. The approach could be evaluated by the existing song title and the evaluation process is same as any recommendation system.