Name
Title | ||
|---|---|---|
Music Structure Boundaries Estimation Using Multiple Self-Similarity Matrices As Input Depth Of Convolutional Neural Networks |
Abstract | ||
|---|---|---|
In this paper, we propose a new representation as input of a Convolutional Neural Network with the goal of estimating music structure boundaries. For this task, previous works used a network performing the late-fusion of a Mel-scaled log-magnitude spectrogram and a self-similarity-lag-matrix. We propose here to use the squaresub-matrices centered on the main diagonals of several self-similarity-matrices, each one representing a different audio descriptors. We propose to combine them using the depth of the input layer. We show that this representation improves the results over the use of the self-similarity-lag-matrix. We also show that using the depth of the input layer provide a convenient way for early fusion of audio representations. |
Year | Venue | DocType |
|---|---|---|
2017 | 2017 AES INTERNATIONAL CONFERENCE ON SEMANTIC AUDIO | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alice Cohen-Hadria | 1 | 0 | 0.34 |
| Geoffroy Peeters | 2 | 523 | 62.99 |