Time-frequency convolutional neural network with bottleneck architecture for query-by-example processing
US10777188B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Nov 14, 2018 |
| Grant date | Sep 15, 2020 |
| Priority date | — |
| Expiry date | Feb 16, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG10L2015/088
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A computing system determines whether a reference audio signal contains a query. A time-frequency convolutional neural network (TFCNN) comprises a time and frequency convolutional layers and a series of additional layers, which include a bottleneck layer. The computation engine applies the TFCNN to samples of a query utterance at least through the bottleneck layer. A query feature vector comprises output values of the bottleneck layer generated when the computation engine applies the TFCNN to the samples of the query utterance. The computation engine also applies the TFCNN to samples of the reference audio signal at least through the bottleneck layer. A reference feature vector comprises output values of the bottleneck layer generated when the computation engine applies the TFCNN to the samples of the reference audio signal. The computation engine determines at least one detection score based on the query feature vector and the reference feature vector.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.