Google Inc.

Richard Channing Moore

Ryan Michael Rifkin

Aren Jansen

Daniel Ellis

Manoj Plakal

Shawn Hershey

Jiayang Liu

Ratheet Pandya

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.

Unsupervised learning of semantic audio representations

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Mountain View, CA, United States of America

Richard Channing Moore