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Unsupervised Adversarial Domain Adaptation Based On The Wasserstein Distance For Acoustic Scene Classification

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Standard

Unsupervised Adversarial Domain Adaptation Based On The Wasserstein Distance For Acoustic Scene Classification. / Drossos, Konstantinos; Magron, Paul; Virtanen, Tuomas.

2019 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA). IEEE, 2019. (IEEE Workshop on Applications of Signal Processing to Audio and Acoustics).

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Harvard

Drossos, K, Magron, P & Virtanen, T 2019, Unsupervised Adversarial Domain Adaptation Based On The Wasserstein Distance For Acoustic Scene Classification. julkaisussa 2019 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA). IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, IEEE, IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, 1/01/00. https://doi.org/10.1109/WASPAA.2019.8937231

APA

Drossos, K., Magron, P., & Virtanen, T. (2019). Unsupervised Adversarial Domain Adaptation Based On The Wasserstein Distance For Acoustic Scene Classification. teoksessa 2019 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) (IEEE Workshop on Applications of Signal Processing to Audio and Acoustics). IEEE. https://doi.org/10.1109/WASPAA.2019.8937231

Vancouver

Drossos K, Magron P, Virtanen T. Unsupervised Adversarial Domain Adaptation Based On The Wasserstein Distance For Acoustic Scene Classification. julkaisussa 2019 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA). IEEE. 2019. (IEEE Workshop on Applications of Signal Processing to Audio and Acoustics). https://doi.org/10.1109/WASPAA.2019.8937231

Author

Drossos, Konstantinos ; Magron, Paul ; Virtanen, Tuomas. / Unsupervised Adversarial Domain Adaptation Based On The Wasserstein Distance For Acoustic Scene Classification. 2019 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA). IEEE, 2019. (IEEE Workshop on Applications of Signal Processing to Audio and Acoustics).

Bibtex - Lataa

@inproceedings{554774abe1464f6bb19b2e37e79d5ba1,
title = "Unsupervised Adversarial Domain Adaptation Based On The Wasserstein Distance For Acoustic Scene Classification",
abstract = "A challenging problem in deep learning-based machine listening field is the degradation of the performance when using data from unseen conditions. In this paper we focus on the acoustic scene classification (ASC) task and propose an adversarial deep learning method to allow adapting an acoustic scene classification system to deal with a new acoustic channel resulting from data captured with a different recording device. We build upon the theoretical model of HΔH-distance and previous adversarial discriminative deep learning method for ASC unsupervised domain adaptation, and we present an adversarial training based method using the Wasserstein distance. We improve the state-of-the-art mean accuracy on the data from the unseen conditions from 32{\%} to 45{\%}, using the TUT Acoustic Scenes dataset.",
keywords = "Acoustic scene classification, unsupervised domain adaptation, Wasserstein distance, adversarial training",
author = "Konstantinos Drossos and Paul Magron and Tuomas Virtanen",
year = "2019",
month = "10",
day = "22",
doi = "10.1109/WASPAA.2019.8937231",
language = "English",
isbn = "978-1-7281-1124-7",
series = "IEEE Workshop on Applications of Signal Processing to Audio and Acoustics",
publisher = "IEEE",
booktitle = "2019 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)",

}

RIS (suitable for import to EndNote) - Lataa

TY - GEN

T1 - Unsupervised Adversarial Domain Adaptation Based On The Wasserstein Distance For Acoustic Scene Classification

AU - Drossos, Konstantinos

AU - Magron, Paul

AU - Virtanen, Tuomas

PY - 2019/10/22

Y1 - 2019/10/22

N2 - A challenging problem in deep learning-based machine listening field is the degradation of the performance when using data from unseen conditions. In this paper we focus on the acoustic scene classification (ASC) task and propose an adversarial deep learning method to allow adapting an acoustic scene classification system to deal with a new acoustic channel resulting from data captured with a different recording device. We build upon the theoretical model of HΔH-distance and previous adversarial discriminative deep learning method for ASC unsupervised domain adaptation, and we present an adversarial training based method using the Wasserstein distance. We improve the state-of-the-art mean accuracy on the data from the unseen conditions from 32% to 45%, using the TUT Acoustic Scenes dataset.

AB - A challenging problem in deep learning-based machine listening field is the degradation of the performance when using data from unseen conditions. In this paper we focus on the acoustic scene classification (ASC) task and propose an adversarial deep learning method to allow adapting an acoustic scene classification system to deal with a new acoustic channel resulting from data captured with a different recording device. We build upon the theoretical model of HΔH-distance and previous adversarial discriminative deep learning method for ASC unsupervised domain adaptation, and we present an adversarial training based method using the Wasserstein distance. We improve the state-of-the-art mean accuracy on the data from the unseen conditions from 32% to 45%, using the TUT Acoustic Scenes dataset.

KW - Acoustic scene classification

KW - unsupervised domain adaptation

KW - Wasserstein distance

KW - adversarial training

U2 - 10.1109/WASPAA.2019.8937231

DO - 10.1109/WASPAA.2019.8937231

M3 - Conference contribution

SN - 978-1-7281-1124-7

T3 - IEEE Workshop on Applications of Signal Processing to Audio and Acoustics

BT - 2019 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)

PB - IEEE

ER -