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The first step of understanding the structure of a music piece is to segment it into formative parts. A recently successful method for finding segment boundaries employs a Convolutional Neural Network (CNN) trained on spectrogram excerpts. While setting a new state of the art, it often misses boundaries defined by non-local musical cues, such as segment repetitions. To account for this, we propose a refined variant of self-similarity lag matrices representing long-term relationships. We thendoi:10.1109/eusipco.2015.7362593 dblp:conf/eusipco/GrillS15 fatcat:zqt7zxui2vexjgt4j7t2aldc4e