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ZENODO
Other ORP type . 2018
License: CC BY NC
Data sources: Datacite
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ZENODO
Other ORP type . 2018
License: CC BY NC
Data sources: Datacite
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Adversarial Unsupervised Domain Adaptation For Acoustic Scene Classification

appsOther research productkeyboard_double_arrow_right Other ORP type 22 Aug 2018Publisher:Zenodo
Authors: Shayan Gharib; Drossos, Konstantinos; Çakir, Emre; Serdyuk, Dmitriy; Virtanen, Tuomas;

doi: 10.5281/zenodo.1401994 , 10.5281/zenodo.1401995

Adversarial Unsupervised Domain Adaptation For Acoustic Scene Classification

Abstract

The two .zip files contain the pre-trained weights, features, and labels for the AUDASC. In order to use them, you need the code of AUDASC (available from here). The code of AUDASC is based on PyTorch framework. For easy and efficient reproducibility, we include our extracted features and labels (one-hot encoded) from the development dataset of the DCASE 2018 Task 1, subtask B. The license specified by the DCASE 2018 Task1, subtask B, for the data is applied here to the extracted features and labels as well.

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citations
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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