Application of LSTM Model for Western Music Composition
LSTM; Machine Learning; Neural Networks; Western Music Composition
Music is one of the innate creative expressions of human beings. Music composition approaches have always been a focal point of music-based research and there has been an increasing interest in Artificial Intelligence (AI) based music composition methods in recent times. Developing an accurate algorithm and neural network architecture is imperative to the success of an AI-based approach to music composition. The present work explores the composition of western music through neural network using a Long Short-Term Memory (LSTM) algorithm. Compositions from seminal western composers such as J.S. Bach, W.A. Mozart, L.V. Beethoven, and F. Chopin were used as the dataset to train the neural network. Seven compositions were generated by the LSTM model and these outputs were presented to a group of thirty volunteers between 18-24 years of age. They were surveyed to identify the music piece as composed by a human or AI and how interesting they found the melodies of each piece. It was found that the LSTM model generated compositions that were thought to be made by a human and create melodies of interest from the perception of the volunteers. It is expected that through this study, more AI-based composition approaches can be developed which encompass more and more of the musical phenomenon. 2022 IEEE.
Cyriac S.; Ashvinee; Kim Y.W.; Tavis R.L.
International Conference on ICT Convergence, Vol-2022-October, pp. 136-141.
Cyriac S., Christ (Deemed to Be University), Centre for Digital Innovation, Bengaluru, India; Ashvinee, Christ (Deemed to Be University), Centre for Digital Innovation, Bengaluru, India; Kim Y.W., Christ (Deemed to Be University), Centre for Digital Innovation, Bengaluru, India; Tavis R.L., Christ (Deemed to Be University), Ashton-Bell Department of Western Music, Bengaluru, India