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Music Structure Boundaries Estimation Using Multiple Self-Similarity Matrices as Input Depth of Convolutional Neural Networks

In this paper we propose a new representation as input of a Convolutional Neural Network in the goal of detecting music structure boundaries. For this task, previous works used a late-fusion of a Mel-scaled Log-Magnitude Spectrograms (MLS) and a lag matrices networks. We propose here to use several self-similarity-matrices, each representing different audio descriptors, and combined using the depth of the input layer. We show that this representation improve the results over the use of the lag-matrix. We also show that using the depth of the input layer provide a convenient way for early fusion of representations.

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AES - Audio Engineering Society