Abstract
Music is a series of harmonious sounds well arranged by musical elements including rhythm, melody, and harmony (RMH). Since music digitalization has resulted in a wide variety of new musical applications used in daily life, the use of music genre classification (MGC), especially MGC automation, is increasingly playing a key role in the development of novel musical services. However, achieving satisfactory performance of MGC automation is a practical challenge. This paper proposes a two-step approach for music genre classification (called TSMGC) on the strength of analytic hierarchy process (AHP) weighted musical features. Compared with other MGC approaches, the TSMGC has three strong points for better performance: (1) various musical features extracted from the RMH and the calculated entropy are comprehensively considered, (2) the weight of features and their impact values determined by AHP are applied on the basis of the Exponential Distribution function, (3) music can be accurately categorized into a main-class and further sub-classes through a two-step classification process. According to the conducted experiment, the result exhibits an accuracy rate of 87%, which demonstrates the potential for the proposed TSMGC method to meet the emerging needs of MGC automation.
Highlights
Music is an essential part of human civilization
This study considers the uses of feature weights, feature extraction, and two-step method for comparisons
For two-step classification, the proposed method can extract the important features and give the analytic hierarchy process (AHP) weights for these features, and the accuracy of the proposed method is 57.19% which is higher than other cases
Summary
Music is an essential part of human civilization. Different types of music composed of various elements (e.g., rhythm, timbre, tempo.) are played on numerous and various occasions, depending on the situation. The digitalization of music has dramatically extended the range of available music applications. According to [1], since users prefer to browse music by genres than other alternatives, the technique of differentiating the music types of genres (music genre classification, MGC). Has become a popular tool of choosing appropriate music for an occasion or application. MGC automation attaches importance to the needs of dealing with a large amount of music within a specified time; for example, MGC is used for musical treatments [2]. Corrêa and Rodrigues [3] presented a review of the most important studies on MGC. In the field of music information retrieval, MGC methods are generally categorized into those that analyze song lyrics or those that analyze musical content [4].
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