Name
Abstract | ||
|---|---|---|
In this work we describe and evaluate methods to learn musical embeddings. Each embedding is a vector that represents four contiguous beats of music and is derived from a symbolic representation. We consider autoencoding-based methods including denoising autoencoders, and context reconstruction, and evaluate the resulting embeddings on a forward prediction and a classification task. |
Year | Venue | Field |
|---|---|---|
2017 | arXiv: Sound | Noise reduction,Embedding,Musical,Computer science,Speech recognition |
DocType | Volume | Citations |
Journal | abs/1706.04486 | 0 |
PageRank | References | Authors |
0.34 | 2 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mason Bretan | 1 | 9 | 2.93 |
| Sageev Oore | 2 | 101 | 18.63 |
| Douglas Eck | 3 | 748 | 64.84 |
| Larry Heck | 4 | 17 | 0.98 |