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Fast threshold optimization for multi-label audio tagging using Surrogate gradient learning

Abstract : Multi-label audio tagging consists of assigning sets of tags to audio recordings. At inference time, thresholds are applied on the confidence scores outputted by a probabilistic classifier, in order to decide which classes are detected active. In this work, we consider having at disposal a trained classifier and we seek to automatically optimize the decision thresholds according to a performance metric of interest, in our case F-measure (micro-F1). We propose a new method, called SGL-Thresh for Surrogate Gradient Learning of Thresholds, that makes use of gradient descent. Since F1 is not differentiable, we propose to approximate the thresholding operation gradients with the gradients of a sigmoid function. We report experiments on three datasets, using state-of-the-art pre-trained deep neural networks. In all cases, SGL-Thresh outperformed three other approaches: a default threshold value (defThresh), an heuristic search algorithm and a method estimating F1 gradients numerically. It reached 54.9\% F1 on AudioSet eval, compared to 50.7% with defThresh. SGL-Thresh is very fast and scalable to a large number of tags. To facilitate reproducibility, data and source code in Pytorch are available online:
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Contributor : Thomas Pellegrini Connect in order to contact the contributor
Submitted on : Friday, February 26, 2021 - 3:38:35 PM
Last modification on : Friday, November 12, 2021 - 1:39:22 PM
Long-term archiving on: : Thursday, May 27, 2021 - 6:49:51 PM



Thomas Pellegrini, Timothée Masquelier. Fast threshold optimization for multi-label audio tagging using Surrogate gradient learning. IEEE International Conference on Acoustics, Speech and Signal Processing, Jun 2021, Toronto, Canada. pp. 651-655, ⟨10.1109/ICASSP39728.2021.9414091⟩. ⟨hal-03153644⟩



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