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Domain-Adversarial Training and Trainable Parallel Front-end for the DCASE 2020 Task 4 Sound Event Detection Challenge

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Abstract

In this paper, we propose several methods for improving Sound Event Detection systems performance in the context of the Detection and Classification of Acoustic Scenes and Events (DCASE) 2020 Task 4 challenge. Our main contributions are in the training techniques, feature pre-processing and prediction post-processing. Given the mismatch between synthetic labelled data and target domain data, we exploit domain adversarial training to improve the network generalization. We show that such technique is especially effective when coupled with dynamic mixing and data augmentation. Together with Hidden Markov Models prediction smoothing, by coupling the challenge baseline with aforementioned techniques we are able to improve event-based macro F1 score by more than 10% on the development set, without computational overhead at inference time. Moreover, we propose a novel, effective Parallel Per-Channel Energy Normalization front-end layer and show that it brings an additional improvement of more than one percent with minimal computational overhead.
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Dates and versions

hal-02962911 , version 1 (09-10-2020)

Identifiers

  • HAL Id : hal-02962911 , version 1

Cite

Samuele Cornell, Michel Olvera, Manuel Pariente, Giovanni Pepe, Emanuele Principi, et al.. Domain-Adversarial Training and Trainable Parallel Front-end for the DCASE 2020 Task 4 Sound Event Detection Challenge. DCASE 2020 - 5th Workshop on Detection and Classification of Acoustic Scenes and Events, Nov 2020, Virtual, Japan. ⟨hal-02962911⟩
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