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Record reviews provide a unique and focused source of linguistic data that can be related to musical recordings, to provide a basis for computational music understanding systems with applications in similarity, recommendation and classification. We analyze a large testbed of music and a corpus of reviews for each work to uncover patterns and develop mechanisms for removing reviewer bias and extraneous non-musical discussion. By building upon work in grounding free text against audio signals we invent an "automatic record review" system that labels new music audio with maximal semantic value for future retrieval tasks. In effect, we grow an unbiased music editor trained from the consensus of the online reviews we have gathered.
Whitman et al. (Thu,) studied this question.
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