High-performance content-based music retrieval via automated navigation and semantic features

Abstract

Content-based music retrieval has been studied for many years. However, it is not easy to achieve effective and efficient retrieval because two issues such as search strategy and music feature are not considered simultaneously. Therefore, in this paper, we propose an innovative music search method using automated navigations and semantic features to cope with these issues. For automated navigations, it is a novel autonomous-feedback technique that moves the search towards the user interest space effectively and efficiently. For semantic features, the low-level audio features are transformed into high-level semantic features to effectively associate with user concepts. To reveal the performance of the proposed method, we conducted a set of comprehensive evaluations on two real music datasets. In the comparative experiments, semantic features are shown to be more effective than audio features. Additionally, the proposed method is superior to state-of-the-art methods in terms of precision, which indicates the average improvements of 151.67% and 148.02% on two datasets, respectively. Moreover, the subjective evaluation shows that the proposed method can earn the users’ satisfactions in the materialized system. In summary, the proposed automated navigations and semantic features are useful for dealing with issues of search strategy and music feature in content-based music retrieval.