A Hybrid Approach Using Binaural i-vectors and Deep Convolutional Neural Networks

Hamid Eghbal-Zadeh; Bernhard Lehner; Matthias Dorfer

A Hybrid Approach Using Binaural i-vectors and Deep Convolutional Neural Networks

Hamid Eghbal-Zadeh
, Bernhard Lehner
, Matthias Dorfer

Institute of Computational Perception

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings › peer-review

Abstract

This report describes the 4 submissions for Task 1 (Audio scene classification) of the DCASE-2016 challenge of the CP-JKU team. We propose 4 different approaches for Audio Scene Classification (ASC). First, we propose a novel i-vector extraction scheme for ASC using both left and right audio channels. Second, we propose a Deep Convolutional Neural Network (DCNN) architecture trained on spectrograms of audio excerpts in end-to-end fashion. Third, we use a calibration transformation to improve the performance of our binaural i-vector system. Finally, we propose a late-fusion of our binaural i-vector and the DCNN. We report the performance of our proposed methods on the provided cross-validation setup for the DCASE-2016 challenge. Using the late-fusion approach, we improve the performance of the baseline by 17 percentage point in accuracy. Our submissions achieved ranks first and second among 49 submissions in the audio scene classification task of DCASE-2016 challenge.

Original language	English
Title of host publication	Proceedings of the Detection and Classification of Acoustic Scenes and Events (DECASE 2016)
Number of pages	5
Publication status	Published - Sept 2016

Fields of science

202002 Audiovisual media
102 Computer Sciences
102001 Artificial intelligence
102003 Image processing
102015 Information systems

JKU Focus areas

Computation in Informatics and Mathematics
Engineering and Natural Sciences (in general)

Cite this

@inproceedings{6a69e93e4c4443f8946a173599653be5,

title = "A Hybrid Approach Using Binaural i-vectors and Deep Convolutional Neural Networks",

abstract = "This report describes the 4 submissions for Task 1 (Audio scene classification) of the DCASE-2016 challenge of the CP-JKU team. We propose 4 different approaches for Audio Scene Classification (ASC). First, we propose a novel i-vector extraction scheme for ASC using both left and right audio channels. Second, we propose a Deep Convolutional Neural Network (DCNN) architecture trained on spectrograms of audio excerpts in end-to-end fashion. Third, we use a calibration transformation to improve the performance of our binaural i-vector system. Finally, we propose a late-fusion of our binaural i-vector and the DCNN. We report the performance of our proposed methods on the provided cross-validation setup for the DCASE-2016 challenge. Using the late-fusion approach, we improve the performance of the baseline by 17 percentage point in accuracy. Our submissions achieved ranks first and second among 49 submissions in the audio scene classification task of DCASE-2016 challenge.",

author = "Hamid Eghbal-Zadeh and Bernhard Lehner and Matthias Dorfer",

year = "2016",

month = sep,

language = "English",

booktitle = "Proceedings of the Detection and Classification of Acoustic Scenes and Events (DECASE 2016)",

}

TY - GEN

T1 - A Hybrid Approach Using Binaural i-vectors and Deep Convolutional Neural Networks

AU - Eghbal-Zadeh, Hamid

AU - Lehner, Bernhard

AU - Dorfer, Matthias

PY - 2016/9

Y1 - 2016/9

N2 - This report describes the 4 submissions for Task 1 (Audio scene classification) of the DCASE-2016 challenge of the CP-JKU team. We propose 4 different approaches for Audio Scene Classification (ASC). First, we propose a novel i-vector extraction scheme for ASC using both left and right audio channels. Second, we propose a Deep Convolutional Neural Network (DCNN) architecture trained on spectrograms of audio excerpts in end-to-end fashion. Third, we use a calibration transformation to improve the performance of our binaural i-vector system. Finally, we propose a late-fusion of our binaural i-vector and the DCNN. We report the performance of our proposed methods on the provided cross-validation setup for the DCASE-2016 challenge. Using the late-fusion approach, we improve the performance of the baseline by 17 percentage point in accuracy. Our submissions achieved ranks first and second among 49 submissions in the audio scene classification task of DCASE-2016 challenge.

AB - This report describes the 4 submissions for Task 1 (Audio scene classification) of the DCASE-2016 challenge of the CP-JKU team. We propose 4 different approaches for Audio Scene Classification (ASC). First, we propose a novel i-vector extraction scheme for ASC using both left and right audio channels. Second, we propose a Deep Convolutional Neural Network (DCNN) architecture trained on spectrograms of audio excerpts in end-to-end fashion. Third, we use a calibration transformation to improve the performance of our binaural i-vector system. Finally, we propose a late-fusion of our binaural i-vector and the DCNN. We report the performance of our proposed methods on the provided cross-validation setup for the DCASE-2016 challenge. Using the late-fusion approach, we improve the performance of the baseline by 17 percentage point in accuracy. Our submissions achieved ranks first and second among 49 submissions in the audio scene classification task of DCASE-2016 challenge.

M3 - Conference proceedings

BT - Proceedings of the Detection and Classification of Acoustic Scenes and Events (DECASE 2016)

ER -