Nearest multi-prototype based music mood classification

Abstract

Music mood classification is a crucial component in the field of multimedia database retrieval and computational musicology. There is a constantly growing interest in developing and evaluating music information retrieval (MIR) systems that can provide automated access to the music mood. The proposed method considers the different types of audio features. From each feature's frame, a bin histogram has been calculated to preserve all important information associated with it. The histogram bins of each feature are used to calculate the similarity matrix, and the number of similarity matrices depends on the number of audio features. Therefore, there are 59 similarity matrixes from the corresponding same amount of audio features. The intra and inter similarity matrix are used to calculate the intra-inter similarity ratio. These similarity ratios are sorted in descending order in each feature. Among them, some of the selected similarity ratios are ultimately used as prototypes from each feature and are used for classification by designing the nearest multi-prototype classifier. The Coimbra mood dataset is used to measure the overall performance of the proposed method. We achieved competitive classification accuracies as compared with other existing state-of-the-art music mood classification techniques.