Unsupervised learning of semantic audio representations
US11335328B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Oct 26, 2018 |
| Grant date | May 17, 2022 |
| Priority date | — |
| Expiry date | Oct 26, 2038 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG10L2015/0635
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Methods are provided for generating training triplets that can be used to train multidimensional embeddings to represent the semantic content of non-speech sounds present in a corpus of audio recordings. These training triplets can be used with a triplet loss function to train the multidimensional embeddings such that the embeddings can be used to cluster the contents of a corpus of audio recordings, to facilitate a query-by-example lookup from the corpus, to allow a small number of manually-labeled audio recordings to be generalized, or to facilitate some other audio classification task. The triplet sampling methods may be used individually or collectively, and each represent a respective heuristic about the semantic structure of audio recordings.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.