Monophonic Music Generation With a Given Emotion Using Conditional Variational Autoencoder - Jacek Grekow; Teodora Dimitrova-Grekow (IEEE Access, 2021)
The rapid increase in the importance of human-machine interaction and the accelerating pace of life pose various challenges for the creators of digital environments. Continuous improvement of human-machine interaction requires precise modeling of the physical and emotional state of people. By implementing emotional intelligence in machines, robots are expected not only to recognize and track emotions when interacting with humans, but also to respond and behave appropriately. The machine should match its reaction to the mood of the user as precisely as possible. Music generation with a given emotion can be a good start to fulfilling such a requirement. This article presents the process of building a system generating music content of a specified emotion. As the emotion labels, four basic emotions: happy, angry, sad, relaxed, corresponding to the four quarters of Russell’s model, were used. Conditional variational autoencoder using a recurrent neural network for sequence processing was used as a generative model. The obtained results in the form of the generated music examples with a specific emotion are convincing in their structure and sound. The generated examples were evaluated with two methods, in the first using metrics for comparison with the training set and in the second using expert annotation.