Search (3 results, page 1 of 1)

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Abstract

Music Information Retrieval (MIR) evaluation has traditionally focused on system-centered approaches where components of MIR systems are evaluated against predefined data sets and golden answers (i.e., ground truth). There are two major limitations of such system-centered evaluation approaches: (a) The evaluation focuses on subtasks in music information retrieval, but not on entire systems and (b) users and their interactions with MIR systems are largely excluded. This article describes the first implementation of a holistic user-experience evaluation in MIR, the MIREX Grand Challenge, where complete MIR systems are evaluated, with user experience being the single overarching goal. It is the first time that complete MIR systems have been evaluated with end users in a realistic scenario. We present the design of the evaluation task, the evaluation criteria and a novel evaluation interface, and the data-collection platform. This is followed by an analysis of the results, reflection on the experience and lessons learned, and plans for future directions.

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Abstract

This research proposes a framework for music mood classification that uses multiple and complementary information sources, namely, music audio, lyric text, and social tags associated with music pieces. This article presents the framework and a thorough evaluation of each of its components. Experimental results on a large data set of 18 mood categories show that combining lyrics and audio significantly outperformed systems using audio-only features. Automatic feature selection techniques were further proved to have reduced feature space. In addition, the examination of learning curves shows that the hybrid systems using lyrics and audio needed fewer training samples and shorter audio clips to achieve the same or better classification accuracies than systems using lyrics or audio singularly. Last but not least, performance comparisons reveal the relative importance of audio and lyric features across mood categories.

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Abstract

There has been little research on task complexity and difficulty in music information retrieval (MIR), whereas many studies in the text retrieval domain have found that task complexity and difficulty have significant effects on user effectiveness. This study aimed to bridge the gap by exploring i) the relationship between task complexity and difficulty; ii) factors affecting task difficulty; and iii) the relationship between task difficulty, task complexity, and user search behaviors in MIR. An empirical user experiment was conducted with 51 participants and a novel MIR system. The participants searched for 6 topics across 3 complexity levels. The results revealed that i) perceived task difficulty in music search is influenced by task complexity, user background, system affordances, and task uncertainty and enjoyability; and ii) perceived task difficulty in MIR is significantly correlated with effectiveness metrics such as the number of songs found, number of clicks, and task completion time. The findings have implications for the design of music search tasks (in research) or use cases (in system development) as well as future MIR systems that can detect task difficulty based on user effectiveness metrics.