![]() With computer-assisted technologies, game designers can create sounds for different scenarios or situations like horror and suspense and provide gamers with information. The quality of the sounds in the game directly impacts the productivity and experience of the player. Sound effects in games are very effective and can be made more attractive by implementing AI approaches. Artificial intelligence and music (AIM) is one of the emerging fields used to generate and manage sounds for different media like the Internet, games, etc. It is very convenient for composers to compose music of high quality using these technologies. ![]() AI-based innovative and intelligent techniques are revolutionising the music industry. Among these applications, music is one that has gained attention in the last couple of years. With the development and advancement of information technology, artificial intelligence (AI) and machine learning are applied in every sector of life. And the average F1-score of LSTM is 95.68%, which is much higher than the DNN-based classification model. The experiment shows that the highest accuracy of the autoencoder-based feature extractor can achieve 95.3%. MVMG is evaluated based on the datasets collected by us: the single-melody MIDI files and the Chinese classical music dataset. And then an LSTM-based music generation and classification model is developed for generating and analyzing music in specific treatment scenario. At first, the music data are modeled to the MDPI and text sequence data by using an autoencoder model, including music features extraction and music clip representation. A Multi-Voice Music Generation system called MVMG based on the algorithm is developed. This paper proposes an long short-term memory (LSTM)-based generation and classification algorithm for multi-voice music data. With the empowerment of artificial intelligence, music therapy technology has made innovative development in the whole process of “diagnosis, treatment and evaluation.” It is necessary to make use of the advantages of artificial intelligence technology to innovate music therapy methods, ensure the accuracy of treatment schemes, and provide more paths for the development of the medical field. Music therapy is to stimulate and hypnotize the human brain by using various forms of music activities, such as listening, singing, playing and rhythm. Music can express people’s thoughts and emotions. ![]() Furthermore, feedback from participants indicated that the application could still improve with the addition of more features, such as the ability to save the generated music for later use. However, the practicality of the application could use some work, as generating music based on the sentiment does not always seem to match up with the original inputted message's sentiment, especially with messages that have a negative sentiment. The results indicated that MFly was largely successful at conveying messages into appropriately fitting music. A post-experiment survey was also provided to each of the participants to gauge the convenience and practicality of the application. To test the effectiveness of this new music-generating method, an experiment was conducted in which twenty-three participants inputted a message with a positive and negative sentiment each and recorded whether each outputted musical piece accurately represented the sentiment from the message. Our solution to this issue was the creation of a mobile application named MFly that can output music using the sentiment from an inputted message. The aim of this paper is to provide a solution to the growing need for fresh music to use in media, as adding music can greatly enhance the media’s atmosphere and the viewers’ experience. ![]()
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