Music Structure Boundary Detection and Labelling by a Deconvolution of Path-Enhanced Self-Similarity Matrix

Tian Cheng; Jordan B. L. Smith; Masataka Goto

ICASSP 2018accepted0 citations

Music Structure Boundary Detection and Labelling by a Deconvolution of Path-Enhanced Self-Similarity Matrix

Tian Cheng, Jordan B. L. Smith, Masataka Goto

Abstract

We propose a music structure analysis method that converts a path-enhanced self-similarity matrix (SSM) into a block-enhanced SSM using non-negative matrix factor 2-D deconvolution (NMF2D). With a non-negative constraint, the deconvolution intuitively corresponds to the repeated stripes in the path-enhanced SSM. Then the block-enhanced SSM is constructed without any clustering technique. We fuse block-enhanced SSMs obtained using different parameters, resulting in better and more robust results. Discussion shows that the proposed method can be a potential tool for analysing music structure at different scales.

BibTeX

@inproceedings{icassp2018_musicstructurebo,
  title = {Music Structure Boundary Detection and Labelling by a Deconvolution of Path-Enhanced Self-Similarity Matrix},
  author = {Tian Cheng and Jordan B. L. Smith and Masataka Goto},
  booktitle = {ICASSP 2018},
  year = {2018}
}