Using Long-Term Structure to Retrieve Music: Representation and Matching
نویسندگان
چکیده
We present a measure of the similarity of the long-term structure of musical pieces. The system deals with raw polyphonic data. Through unsupervised learning, we generate an abstract representation of music the “texture score”. This “texture score” can be matched to other similar scores using a generalized edit distance, in order to assess structural similarity. We notably apply this algorithm to the retrieval of different interpretations of the same song within a music database.
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