| 发明名称 |
Non-negative Matrix Factorization Regularized by Recurrent Neural Networks for Audio Processing |
| 摘要 |
Sound processing techniques using recurrent neural networks are described. In one or more implementations, temporal dependencies are captured in sound data that are modeled through use of a recurrent neural network (RNN). The captured temporal dependencies are employed as part of feature extraction performed using nonnegative matrix factorization (NMF). One or more sound processing techniques are performed on the sound data based at least in part on the feature extraction. |
| 申请公布号 |
US2015242180(A1) |
申请公布日期 |
2015.08.27 |
| 申请号 |
US201414186832 |
申请日期 |
2014.02.21 |
| 申请人 |
Adobe Systems Incorporated |
发明人 |
Boulanger-Lewandowski Nicolas Maurice;Mysore Gautham J.;Hoffman Matthew Douglas |
| 分类号 |
G06F3/16;G06N3/02 |
主分类号 |
G06F3/16 |
| 代理机构 |
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代理人 |
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| 主权项 |
1. A method implemented by one or more computing devices, the method comprising:
capturing temporal dependencies in sound data modeled through use of a recurrent neural network (RNN); employing the captured temporal dependencies as part of feature extraction performed using nonnegative matrix factorization (NMF); and performing one or more sound processing techniques on the sound data based at least in part on the feature extraction. |
| 地址 |
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