Abstract
Music is closely related to human life and is an important way for people to express their feelings in life. Deep neural networks have played a significant role in the field of music processing. There are many different neural network models to implement deep learning for audio processing. For general neural networks, there are problems such as complex operation and slow computing speed. In this paper, we introduce Long Short-Term Memory (LSTM), which is a circulating neural network, to realize end-to-end training. The network structure is simple and can generate better audio sequences after the training model. After music generation, human voice conversion is important for music understanding and inserting lyrics to pure music. We propose the audio segmentation technology for segmenting the fixed length of the human voice. Different notes are classified through piano music without considering the scale and are correlated with the different human voices we get. Finally, through the transformation, we can express the generated piano music through the output of the human voice. Experimental results demonstrate that the proposed scheme can successfully obtain a human voice from pure piano Music generated by LSTM.
Highlights
Music, as an art form expressing emotions, is in high demand in the market
Various related models have been applied to the study of the music generation problem
Long Short-Term Memory (LSTM) adds the idea of self-circulation to keep the gradient flowing, which can effectively solve the problems of long-term dependence and gradient explosion[7].Through the use of the LSTM music generation model to train a large number of piano music, automatic generation of new piano music
Summary
As an art form expressing emotions, is in high demand in the market. At present, the number of professional music creators is limited, and music production takes time and effort, and the cost is high[1]. The development of deep learning in music allows us to use models to generate piano music. With the development of deep learning, the music generation problem has come back into our field of vision. LSTM adds the idea of self-circulation to keep the gradient flowing, which can effectively solve the problems of long-term dependence and gradient explosion[7].Through the use of the LSTM music generation model to train a large number of piano music, automatic generation of new piano music. This paper mainly uses a mature model of piano music generation—LSTM and converse piano music to the human voice[8][9]. Because our experiment is currently only able to perform voice conversion for single-key piano repertoire. The transformation between piano music and our voice information lays a foundation for the step to realize the automatic generation of music with complete lyrics and emotions
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