Piano Automatic Computer Composition by Deep Learning and Blockchain Technology

Li, Huizi

doi:10.1109/access.2020.3031155

Cited by 16 publications

(7 citation statements)

References 23 publications

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“…In contrast, the total time taken by the eight experiments is basically about 4–5 seconds, and the time-consuming of the work uplink and confirmation process is controlled at about 4 seconds. In addition, Li [ 33 ] conducted research on automatic piano composition and recognition technology based on DL and introduced the RNN, LSTM, and Gated Recurrent Unit (GRU) network and DL-based neural networks. The findings indicate that the GRU-RNN model shows satisfactory results in both manual analysis evaluation or paragraph pause analysis.…”

Section: Resultsmentioning

confidence: 99%

Music Recognition Using Blockchain Technology and Deep Learning

Chen

Qian

et al. 2022

Computational Intelligence and Neuroscience

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The purposes are to recognize and classify different music characteristics and strengthen the copyright protection system for original digital music in the big data era. Deep learning (DL) and blockchain technology are applied and researched herein. Based on CNN (Convolutional Neural Network), a music recognition method combined with hashing learning is proposed. The error generated when outputting the binary hash code is considered, and the semantic similarity of the hash code is ensured. Besides, the application of blockchain technology in the current intellectual property protection in original music is discussed. According to digital music property rights protection needs, the system is divided into modules, and its functions are designed. The system ensures its various functions by applying the application protocol designed in the Algor and network. In the experiments, the MagnaTagATune dataset is selected to verify the performance of the proposed CRNNH (Convolutional Recurrent Neural Network Hashing) algorithm. The algorithm shows the best music recognition performance under different bit numbers. When the number of connections is about 100, the QPS value of the blockchain-based music property rights protection system can be stabilized at about 20,000. At any number of threads, the system pressure will increase dramatically with the increase in the number of analog connections. The music recognition algorithm based on DL and hash method discussed is of great significance in improving the classification accuracy of music recognition. The application of blockchain technology in the copyright protection platform of original music works can protect the copyright of digital music and ensure the operation performance of the system.

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Section: Resultsmentioning

confidence: 99%

Music Recognition Using Blockchain Technology and Deep Learning

Chen

Qian

et al. 2022

Computational Intelligence and Neuroscience

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“…Through experiments, the authors proved that this audiocombined video data transcription method could improve the quality of music transcribed from each mode alone. Li (2020) took piano composition as a sample to explore computer automatic composition and analyzed the application of DL and blockchain to the generation of digital music works. They reported that DL had great application potential in computer automatic composition of digital music, and the integration of blockchain and DL had played a role in promoting the expansion and popularization of the digital music market.…”

Section: Related Workmentioning

confidence: 99%

Analysis of Piano Performance Characteristics by Deep Learning and Artificial Intelligence and Its Application in Piano Teaching

2022

Front. Psychol.

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Deep learning (DL) and artificial intelligence (AI) are jointly applied to concrete piano teaching for children to comprehensively promote modern piano teaching and improve the overall teaching quality. First, the teaching environment and the functions of the intelligent piano are expounded. Then, a piano note onset detection method is proposed based on the convolution neural network (CNN). The network can analyze the time-frequency of the input piano music signal by transforming the original time-domain waveform of the piano music signal into the frequency distribution varying with time. Besides, it can detect the note onset at a stable state after 8 × 104 iterations. Moreover, an intelligent piano teaching method is designed to teach Jingle Bells for 40 preschool children aged 4–6 years. Finally, a questionnaire survey is performed to investigate the teaching situation, including the learning interest and learning effect of children and learning feedback from parents. The results show that 80% of children like smart music scores, 82% of children like intelligent piano lessons with games, and 84% of children can learn actively in the intelligent piano class. Besides, 85% of parents believe that their children are more interested in learning piano. In general, the intelligent piano teaching method effectively combines DL with AI to realize the overall optimization of piano performance. It is widely favored by preschool children and their parents and plays an important role in improving the interest of preschool children in piano learning.

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“…In [8], first, through the elaboration of the neural network techniques dependent on deep learning, the recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU) networks were presented, and the deep learning-based GRU-RNN programmed piece model was built. Second, the blockchain innovation was broken down and communicated, and the issues in the conventional copyright security and the board of computerized music were dissected.…”

Section: Related Workmentioning

confidence: 99%

Generation of Digital Art Composition Using a Multilabel Learning Algorithm

Gong

2021

Mathematical Problems in Engineering

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The traditional methods for generating digital art composition have the disadvantage of capturing incomplete geometric information, which leads to obvious defects in the generation results. Therefore, a digital art composition generation method based on the multilabel learning algorithm is proposed in this research. Firstly, a preset series of grids are prepared to generate sampling and fractal pixels on the drawing base. Then, the preset grid construction is constructed by the interactive program of the preset grid library. After the stroke is drawn by the user, the actual motion trajectory of the pen is sampled by the digital panel, and the stroke information in the motion trajectory is obtained by the multilabel learning algorithm. Next, the steps of generating art composition are designed, including generating the skeleton of art composition, generating the geometric network structure of the skeleton, generating the sampling pixel and connecting the fractal pixel, and initializing other attributes of the mesh. Experimental results show that the proposed method has higher sampling rate and geometric information capture rate and has better application performance and prospect.

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Piano Automatic Computer Composition by Deep Learning and Blockchain Technology

Cited by 16 publications

References 23 publications

Music Recognition Using Blockchain Technology and Deep Learning

Music Recognition Using Blockchain Technology and Deep Learning

Analysis of Piano Performance Characteristics by Deep Learning and Artificial Intelligence and Its Application in Piano Teaching

Generation of Digital Art Composition Using a Multilabel Learning Algorithm

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