A high-capacity performance-preserving blind technique for reversible information hiding via MIDI files using delta times

Liu, Yi‐Hsin; Wu, Da-Chun

doi:10.1007/s11042-019-08526-9

Cited by 5 publications

(1 citation statement)

References 22 publications

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“…Most methods use the musical instrument digital interface (MIDI) as the notation of music [ 15 ]. Its advantages are that it is straightforward, the result of the model is the musical score, and the training data are easy to obtain.…”

Section: Introduction Of Related Algorithms and Model Establishment A...mentioning

confidence: 99%

The Generation of Piano Music Using Deep Learning Aided by Robotic Technology

Jian¹,

Zhang³

et al. 2022

Computational Intelligence and Neuroscience

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In order to improve the accuracy and precision of music generation assisted by robotics, this study analyzes the application of deep learning in piano music generation. Firstly, based on the basic concepts of robotics and deep learning, the advantages of long short-term memory (LSTM) networks are introduced and applied to the piano music generation. Meanwhile, based on LSTM, dropout coefficients are used for optimization. Secondly, various parameters of the algorithm are determined, including the effects of the number of iterations and neurons in the hidden layer on the effect of piano music generation. Finally, the generated music sequence spectrograms are analyzed to illustrate the accuracy and rationality of the algorithm. The spectrograms are compared with the music sequence spectrograms generated by the traditional restricted Boltzmann machine (RBM) music generation algorithm. The results show that (1) when the dropout coefficient value is 0.7, the function converges faster, and the experimental results are better; (2) when the number of iterations is 6000, the error between the generated music sequence and the original music is the smallest; (3) the number of hidden layers of the network is set to 4. When the number of neurons in each hidden layer is set to 1024, the training result of the network is optimal; (4) compared with the traditional RBM piano music generation algorithm, the LSTM-based algorithm and the sampling frequency distribution tend to be consistent with the original sample. The results show that the network has good performance in music generation and can provide a certain reference for automatic music generation.

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Section: Introduction Of Related Algorithms and Model Establishment A...mentioning

confidence: 99%