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Chord-based music generation using long short-term memory neural networks in the context of artificial intelligence

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Abstract

With the rapid development of artificial intelligence (AI), music generation has gained widespread attention. Long short-term memory (LSTM) has advantages in handling time series data and has achieved success in the field of music generation. This neural network is capable of capturing the long-term dependencies in music, thus generating chord music that is coherent and innovative. Therefore, to develop a creative and artistic music generation model, this study initially establishes a hidden Markov model (HMM) for chord recognition in music. Subsequently, the algorithm, leveraging the multi-style chord music generation (MSCMG) network, is proposed and applied for chord music generation. Furthermore, an evaluation of the chord music generation algorithm is conducted, utilizing LSTM neural networks within the context of AI. The findings indicate that the HMM, devised in this study, attains an impressive 81.8% chord recognition rate for piano compositions. Additionally, the algorithm, based on the MSCMG network, achieves a notable similarity score of 82.1% for generating classical-style music, with corresponding scores of 3.45, 3.42, and 3.44 for folk-style, classical-style, and pop-style music, respectively. This investigation lays the groundwork for the fusion of AI technology and music composition, exploring novel avenues for music generation and providing novel tools and insights for creative and theoretical exploration within the realm of music.

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Acknowledgements

The authors acknowledge the help from the university colleagues.

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  1. School of Art, Northwest University, Xi’an, 710127, China

    Fanfan Li

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  1. Fanfan Li
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Li, F. Chord-based music generation using long short-term memory neural networks in the context of artificial intelligence. J Supercomput 80, 6068–6092 (2024). https://doi.org/10.1007/s11227-023-05704-3

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