Service interruption on Monday 11 July from 12:30 to 13:00: all the sites of the CCSD (HAL, EpiSciences, SciencesConf, AureHAL) will be inaccessible (network hardware connection).
Skip to Main content Skip to Navigation
Conference papers

On the impact of normalization strategies in unsupervised adversarial domain adaptation for acoustic scene classification

Abstract : Acoustic scene classification systems face performance degradation when trained and tested on data recorded by different devices. Unsupervised domain adaptation methods have been studied to reduce the impact of this mismatch. While they do not assume the availability of labels at test time, they often exploit parallel data recorded by both devices, and thus are not fully blind to the target domain. In this paper, we address a more practical scenario where parallel data are not available. We thoroughly analyze the impact of normalization and moment matching strategies to compensate for the linear distortion introduced by the recording device and propose their integration with adversarial domain adaptation to handle the remaining non-linear distortion. Experiments on the DCASE Challenge 2018 Task 1B dataset show that the proposed integrated approach considerably reduces domain mismatch, reaching an accuracy in the target domain close to that obtained in the source domain.
Document type :
Conference papers
Complete list of metadata

https://hal.inria.fr/hal-03668251
Contributor : Emmanuel Vincent Connect in order to contact the contributor
Submitted on : Saturday, May 14, 2022 - 3:54:05 PM
Last modification on : Tuesday, May 24, 2022 - 11:00:53 AM

File

olvera_ICASSP22.pdf
Files produced by the author(s)

Identifiers

Citation

Michel Olvera, Emmanuel Vincent, Gilles Gasso. On the impact of normalization strategies in unsupervised adversarial domain adaptation for acoustic scene classification. ICASSP 2022 - IEEE International Conference on Acoustics, Speech and Signal Processing, May 2022, Singapore, Singapore. ⟨10.1109/ICASSP43922.2022.9747540⟩. ⟨hal-03668251⟩

Share

Metrics

Record views

18

Files downloads

17