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ZEINALI, H. BURGET, L. ČERNOCKÝ, J.
Originální název
Convolutional Neural Networks and X-Vector Embedding for DCASE2018 Acoustic Scene Classification Challenge
Typ
článek ve sborníku mimo WoS a Scopus
Jazyk
angličtina
Originální abstrakt
In this paper, the Brno University of Technology (BUT) team submissions for Task 1 (Acoustic Scene Classification, ASC) of the DCASE-2018 challenge are described. Also, the analysis of different methods on the leaderboard set is provided. The proposed approach is a fusion of two different Convolutional Neural Network (CNN) topologies. The first one is the common two-dimensional CNNs which is mainly used in image classification. The second one is a one-dimensional CNN for extracting fixed-length audio segment embeddings, so called x-vectors, which has also been used in speech processing, especially for speaker recognition. In addition to the different topologies, two types of features were tested: log mel-spectrogram and CQT features. Finally, the outputs of different systems are fused using a simple output averaging in the best performing system. Our submissions ranked third among 24 teams in the ASC sub-task A (task 1a).
Klíčová slova
Audio scene classification, Convolutional neural networks, Deep learning, x-vectors, Regularized LDA
Autoři
ZEINALI, H.; BURGET, L.; ČERNOCKÝ, J.
Vydáno
19. 11. 2018
Nakladatel
Tampere University of Technology
Místo
Surrey
ISBN
978-952-15-4262-6
Kniha
Proceedings of DCASE 2018 Workshop
Strany od
1
Strany do
5
Strany počet
URL
http://dcase.community/documents/workshop2018/proceedings/DCASE2018Workshop_Zeinali_149.pdf
BibTex
@inproceedings{BUT155111, author="Hossein {Zeinali} and Lukáš {Burget} and Jan {Černocký}", title="Convolutional Neural Networks and X-Vector Embedding for DCASE2018 Acoustic Scene Classification Challenge", booktitle="Proceedings of DCASE 2018 Workshop", year="2018", pages="1--5", publisher="Tampere University of Technology", address="Surrey", isbn="978-952-15-4262-6", url="http://dcase.community/documents/workshop2018/proceedings/DCASE2018Workshop_Zeinali_149.pdf" }