1 Sektion Aalborg, The Faculty of Engineering and Science, Aalborg University, VBN2 Media Technology, The Faculty of Engineering and Science, Aalborg University, VBN3 Department of Architecture, Design and Media Technology, The Faculty of Engineering and Science, Aalborg University, VBN4 The Faculty of Engineering and Science, Aalborg University, VBN
Pattern discovery by geometric compression
Three versions of each of two greedy compression algorithms, COSIATEC and SIATECCompress, were run on the JKU Patterns Development Database. Each algorithm takes a point-set representation of a piece of music as input and computes a compressed encoding of the piece in the form of a union of translational equivalence classes of maximal translatable patterns. COSIATEC iteratively uses the SIATEC algorithm to strictly partition the input set into the covered sets of a set of MTP TECs. On each iteration, COSIATEC finds the “best” TEC and then removes its covered set from the input dataset. SIATECCompress runs SIATEC just once to get a list of MTP TECs and then selects a subset of the “best” TECs that is sufficient to cover the input dataset. Both algorithms select TECs primarily on the basis of compression ratio and compactness.
Music Information Retrieval Evaluation Exchange (mirex 2013), 2013
musical pattern discovery; music information retrieval; algorithms; data mining; pattern discovery; music analysis; computational music analysis; computational musicology
Main Research Area:
International Society for Music Information Retrieval Conference, 2013
International Society for Music Information Retrieval