LSTM-Based Music Generation Technologies
In deep learning, Long Short-Term Memory (LSTM) is a well-established and widely used approach for music generation. Nevertheless, creating musical compositions that match the quality of those created by human composers remains a formidable challenge. The intricate nature of musical components, incl...
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MDPI AG
2025-06-01
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| author | Yi-Jen Mon |
| author_facet | Yi-Jen Mon |
| author_sort | Yi-Jen Mon |
| collection | DOAJ |
| description | In deep learning, Long Short-Term Memory (LSTM) is a well-established and widely used approach for music generation. Nevertheless, creating musical compositions that match the quality of those created by human composers remains a formidable challenge. The intricate nature of musical components, including pitch, intensity, rhythm, notes, chords, and more, necessitates the extraction of these elements from extensive datasets, making the preliminary work arduous. To address this, we employed various tools to deconstruct the musical structure, conduct step-by-step learning, and then reconstruct it. This article primarily presents the techniques for dissecting musical components in the preliminary phase. Subsequently, it introduces the use of LSTM to build a deep learning network architecture, enabling the learning of musical features and temporal coherence. Finally, through in-depth analysis and comparative studies, this paper validates the efficacy of the proposed research methodology, demonstrating its ability to capture musical coherence and generate compositions with similar styles. |
| format | Article |
| id | doaj-art-019fc8d16e684f718aa4f5ac71aa2a27 |
| institution | Kabale University |
| issn | 2073-431X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj-art-019fc8d16e684f718aa4f5ac71aa2a272025-08-20T03:26:26ZengMDPI AGComputers2073-431X2025-06-0114622910.3390/computers14060229LSTM-Based Music Generation TechnologiesYi-Jen Mon0Department of Electronic Engineering, Ming-Chuan University, Guei-Shan District, Taoyuan City 333, TaiwanIn deep learning, Long Short-Term Memory (LSTM) is a well-established and widely used approach for music generation. Nevertheless, creating musical compositions that match the quality of those created by human composers remains a formidable challenge. The intricate nature of musical components, including pitch, intensity, rhythm, notes, chords, and more, necessitates the extraction of these elements from extensive datasets, making the preliminary work arduous. To address this, we employed various tools to deconstruct the musical structure, conduct step-by-step learning, and then reconstruct it. This article primarily presents the techniques for dissecting musical components in the preliminary phase. Subsequently, it introduces the use of LSTM to build a deep learning network architecture, enabling the learning of musical features and temporal coherence. Finally, through in-depth analysis and comparative studies, this paper validates the efficacy of the proposed research methodology, demonstrating its ability to capture musical coherence and generate compositions with similar styles.https://www.mdpi.com/2073-431X/14/6/229deep learningLSTMmusic generationanacondaTensorFlowmusic21 |
| spellingShingle | Yi-Jen Mon LSTM-Based Music Generation Technologies Computers deep learning LSTM music generation anaconda TensorFlow music21 |
| title | LSTM-Based Music Generation Technologies |
| title_full | LSTM-Based Music Generation Technologies |
| title_fullStr | LSTM-Based Music Generation Technologies |
| title_full_unstemmed | LSTM-Based Music Generation Technologies |
| title_short | LSTM-Based Music Generation Technologies |
| title_sort | lstm based music generation technologies |
| topic | deep learning LSTM music generation anaconda TensorFlow music21 |
| url | https://www.mdpi.com/2073-431X/14/6/229 |
| work_keys_str_mv | AT yijenmon lstmbasedmusicgenerationtechnologies |