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Semi-supervised triplet loss based learning of ambient audio embeddings


Deep neural networks are particularly useful to learn relevant repre-sentations from data. Recent studies have demonstrated the poten-tial of unsupervised representation learning for ambient sound anal-ysis using various flavors of the triplet loss. They have comparedthis approach to supervised learning. However, in real situations,it is common to have a small labeled dataset and a large unlabeledone. In this paper, we combine unsupervised and supervised tripletloss based learning into a semi-supervised representation learningapproach. We propose two flavors of this approach, whereby thepositive samples for those triplets whose anchors are unlabeled areobtained either by applying a transformation to the anchor, or byselecting the nearest sample in the training set. We compare ourapproach to supervised and unsupervised representation learning aswell as the ratio between the amount of labeled and unlabeled data.We evaluate all the above approaches on an audio tagging task usingthe DCASE 2018 Task 4 dataset, and we show the impact of thisratio on the tagging performance.
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hal-02025824 , version 1 (22-02-2019)


  • HAL Id : hal-02025824 , version 1


Nicolas Turpault, Romain Serizel, Emmanuel Vincent. Semi-supervised triplet loss based learning of ambient audio embeddings. ICASSP 2019, May 2019, Brighton, United Kingdom. ⟨hal-02025824⟩
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