Abstract
Deep learning techniques for generating music that has melody and harmony and is similar to music compositions by human beings is something that has fascinated researchers in the field of artificial intelligence. Nowadays, deep learning is being used for solving various problems in numerous artistic fields. There has been a new trend of using deep learning models for various applications in the field of music that has attracted much attention, and automated music generation has been an active area of research that lies in the cross section of artificial intelligence and audio synthesis. Previously, the work in automated music generation was solely focused on generating music, which consisted of a single melody, which is also known as monophonic music. More recently, research work related to the automated generation of polyphonic music, music, which consists of multiple melodies, has met partial success with the help of estimation of time series probability density. In this paper, we use Restricted Boltzmann Machine (RBM) and Recurrent Neural Network Restricted Boltzmann Machine for music generation by training it on a collection of Musical Instrument Digital Interface (MIDI) files.
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Ramasubbareddy, S., Saidulu, D., Devasekhar, V., Swathi, V., Maini, S.S., Govinda, K. (2019). Music Generation Using Deep Learning Techniques. In: Saini, H., Sayal, R., Govardhan, A., Buyya, R. (eds) Innovations in Computer Science and Engineering. Lecture Notes in Networks and Systems, vol 74. Springer, Singapore. https://doi.org/10.1007/978-981-13-7082-3_37
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DOI: https://doi.org/10.1007/978-981-13-7082-3_37
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