Emotion-Driven Music Data Mining And Retrieval

Music emotion recognition (MER) is to enable the computer to endow music emotion cognition ability as human being. The research purpose of music emotion recognition is to make use of music emotion information more effectively, and apply it in many other multimedia research fields. At present, one of the most important applications of MER research is music emotion retrieval and music management. According to the finding of music behavior research, "music emotion" has become one of the most commonly used ways of music retrieval. Most of traditional music retrieval methods are based on the manual classification, like music style, song name and album name, and could not retrieve according to the essential music emotion information. As a result, it is significant to retrieve music by means of music emotion type. The research on MER has both great scientific research value and broad prospects on application.Since the 20th century, the research on MER is getting more and more interest and concern from scientists. It triggers a multidisciplinary and multilevel research discusses which involves cognitive behavior science, computer science, neuroscience, psychology, medicine and biology. Among all the related disciplines, machine learning methods in artificial intelligence creates more possibility on the research of MER. Scholars use music feature extraction tools to obtain music emotion features from music pieces and build model of MER by machine learning algorithm to realize the automatic music emotion recognition by computer. They have made many excellent research achievements by different methods and multidisciplinary crossover study, which promotes the development of MER theory and application. However, the research of MER is still in its early stages. The MER algorithm and model of better performance should be built on a larger music resource data base and larger set of music feature information.In this paper, the author studies MER by means of machine learning algorithm, explores the relations between music feature information and music emotion recognition. Based on the Chinese folk music emotion database, the author compares the models of Support Vector Machine (SVM) algorithm, Back Propagation (BP) neural network algorithm and Linear Regression algorithm on music emotion recognition and finds the SVM has the best performance. Then, an optimal model based on Support Vector Machine (SVM) algorithm is constructed, which could effectively identify music emotion.This paper combines with the knowledge of computer science, music characteristics, methods of artificial intelligence, cognitive psychology and the characteristics of Chinese culture. After a serious of research work, including Chinese folk music resources collection, Chinese folk music database construction, Chinese folk music feature information extraction, music emotion feature modeling, a Chinese folk music emotion retrieval system and a Medical Music DJ System application are developed. The specific research results and innovation points are as follows:(1) On the basis of computer music research, an in-depth study on Chinese folk music is carried out. Firstly, a wide collection of Chinese folk music resources is made and a Chinese folk music library is built. Then based on Hevner music emotion model, a manual emotion labeling experiment of Chinese folk music is organized and the first Chinese folk music emotion feature library is established.(2) Based on Chinese folk music emotion feature library, the author introduces machine learning methods to build the model of MER. Three algorithms are compared, and then a MER model with good recognition rate is selected. After further improvement on model antinoise ability and generalization ability, a MER model with good performance is built up.(3) A Chinese folk music emotion retrieval system is developed based on Chinese folk music MER model and Chinese folk music library. System can retrieve Chinese folk music library by means of emotion keywords, the user could choose emotion categories and emotional intensity to get the corresponding retrieval feedback.In addition, the author introduces the physiological characteristics information into the music retrieval system and builds up the application of Medical Music DJ System, in view of research of music therapy. The system aims to construct a personalized medical music recommendation system based on music emotion recognition. The research results also could be applied in the fields of signal processing, pattern recognition, human-machine interaction, multimedia technology, artificial intelligence as research guidance.