We dissect the system to understand its operation, determine its sensitivities and limitations, and predict the kinds of knowledge it could and could not possess about music. In this paper, we take a state-of-the-art music content analysis system and investigate what causes it to achieve exceptionally high performance in a benchmark music audio dataset. However, it might be that these numbers do not mean what they seem. Some systems produce remarkable accuracies at recognising high-level semantic concepts, such as music style, genre and mood. Hundreds of papers have so far been published toward this goal, and great progress appears to have been made. Building systems that possess the sensitivity and intelligence to identify and describe high-level attributes in music audio signals continues to be an elusive goal, but one that surely has broad and deep implications for a wide variety of applications.
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