Dr. Hong Zhao Profile

Dr. Hong Zhao

at Lanzhou Univ of Technology

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Publications (2)

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Proceedings Article | 2 December 2022 Paper

Research on automatic music generation with multi-track based on melody constraints

Hong Zhao, Wanning Su, Xin Zhang

Proceedings Volume 12288, 122881G (2022) https://doi.org/10.1117/12.2640919

KEYWORDS: Computer programming, Transformers, Data modeling, Statistical modeling, Neural networks, Instrument modeling, Visual process modeling, Seaborgium, Evolutionary algorithms, Automatic tracking

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Aiming at the problems that the melody generated by the existing multi track music generation model is not pleasant and the harmony between tracks is not enough, a multi track music generation model with TransformerX-SpanBERT sequence conditions is proposed. The module is a Generative Adversarial Network structure. Since the generated music has multiple tracks, first, use TransformerXL as the generator and SpanBERT as the discriminator to generate piano track music, and the other tracks have one generator for each track. Therefore, there are multiple generators and one discriminator. First, piano track music is as input, let each track generator to generate their own music. Then, the common discriminator is used to judge the music quality generated by each track and the harmony between tracks. Finally, the global attention mechanism is used to pay attention to the music characteristics to ensure the overall effect of music. The objective evaluation index of music and the subjective score of listeners are used to verify the model and compare it withTransformer-GAN and MuseGAN Multi-track sequential symbol music, the results show that the music generated by the model in this paper has the advantages of more harmonious tracks and better melody, rhythm and coherence, therefore, it can be applied to practical music creation.

Proceedings Article | 23 May 2022 Paper

Prediction of air pollutant concentration in Jinchang City

Hong Zhao, Pei Yang, Tengjiao Liu

Proceedings Volume 12254, 1225425 (2022) https://doi.org/10.1117/12.2638676

KEYWORDS: Data modeling, Performance modeling, Air contamination, Process modeling, Carbon monoxide, Nitrogen dioxide, Neural networks, Computer programming

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Air pollution has aroused widespread concern. Predicting air pollution can help people prepare for preventive responses. However, the existing air quality prediction methods are still in their infancy. In this paper, the LSTM (Long Short-Term Memory) sequence-to-sequence model based on the attention mechanism is applied to the prediction of air pollution. We used the meteorological and air pollutant observation data and pollutant discharge point monitoring data of Jinchang City, Gansu Province, and fused the meteorological and air pollution data of the neighboring areas to analyze the six basic air pollutants (PM2.5, PM10 , SO2, NO2, O3, CO), followed by model training and prediction evaluation. The results show that the prediction performance of the model for each air pollutant is different. For the prediction of short-term NO2, O3 and CO concentrations in the future, the A-LSTM-Seq2Seq model designed in this paper performs better; for PM2.5, PM10 and SO2, the prediction error of the model is larger than the previous three, because The observed values of these three are widely distributed and the data is discrete. In the future, it is necessary to improve the prediction performance from the perspective of improving the model. The model pays attention to all six basic air pollutants, and has certain practical value and application potential in the prediction of air pollution on the hourly time scale, and also has reference significance for the prediction of AQI.

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