Title: Automatic music generation using a bio-inspired algorithm-based deep learning model
Authors: V. Bhuvana Kumar; M. Kathiravan
Addresses: Department of Computer Science and Engineering, Hindustan Institute of Technology and Science, Padur, Kelambaakkam, Chennai, 603103, Tamilnadu, India ' Department of Computer Science and Engineering, Hindustan Institute of Technology and Science, Padur, Kelambaakkam, Chennai, 603103, Tamilnadu, India
Abstract: In recent years, automatic music generation has played a vital role in getting multimedia products cheaper and faster. For automatic music generation, both machine learning and deep learning methods were presented. The researchers, in particular, have used long short-term memory (LSTM). Although the LSTM model produces better results, its prediction accuracy for music generation needs to be improved further. Thus, an optimised LSTM model is presented for automatic music generation. Namely, to improve the efficiency of LSTM, an adaptive crocodile optimisation algorithm (ACOA) is presented. Using ACOA, the weight parameters of the LSTM are optimised. It leads to enhanced efficiency in music generation or music vector prediction. The proposed scheme is evaluated using a classical music musical instrument digital interface (MIDI) dataset. The paper's findings show that the proposed ACOA-LSTM outperforms the conventional LSTM in prediction accuracy.
Keywords: automatic music generation; LSTM; long short-term memory; ACOA; adaptive crocodile optimisation algorithm; MIDI; musical instrument digital interface.
DOI: 10.1504/IJSSE.2024.140754
International Journal of System of Systems Engineering, 2024 Vol.14 No.5, pp.480 - 503
Received: 06 Mar 2023
Accepted: 31 Mar 2023
Published online: 02 Sep 2024 *