DNN transfer learning based non-linear feature extraction for acoustic event classification

Seongkyu Mun; Minkyu Shin; Suwon Shon; Wooil Kim; David K. Han; Hanseok Ko

doi:10.1587/transinf.2017EDL8048

DNN transfer learning based non-linear feature extraction for acoustic event classification

Seongkyu Mun, Minkyu Shin, Suwon Shon, Wooil Kim, David K. Han, Hanseok Ko

Research output: Contribution to journal › Article › peer-review

14 Citations (Scopus)

Abstract

Recent acoustic event classification research has focused on training suitable filters to represent acoustic events. However, due to limited availability of target event databases and linearity of conventional filters, there is still room for improving performance. By exploiting the non-linear modeling of deep neural networks (DNNs) and their ability to learn beyond pre-Trained environments, this letter proposes a DNN-based feature extraction scheme for the classification of acoustic events. The effectiveness and robustness to noise of the proposed method are demonstrated using a database of indoor surveillance environments.

Original language	English
Pages (from-to)	2249-2252
Number of pages	4
Journal	IEICE Transactions on Information and Systems
Volume	E100D
Issue number	9
DOIs	https://doi.org/10.1587/transinf.2017EDL8048
Publication status	Published - 2017 Sept

Bibliographical note

Publisher Copyright:
Copyright © 2017 The Institute of Electronics, Information and Communication Engineers.

Keywords

Acoustic event classification
Acoustic feature
Deep neural network
Transfer learning

ASJC Scopus subject areas

Software
Hardware and Architecture
Computer Vision and Pattern Recognition
Electrical and Electronic Engineering
Artificial Intelligence

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10.1587/transinf.2017EDL8048

Cite this

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title = "DNN transfer learning based non-linear feature extraction for acoustic event classification",

abstract = "Recent acoustic event classification research has focused on training suitable filters to represent acoustic events. However, due to limited availability of target event databases and linearity of conventional filters, there is still room for improving performance. By exploiting the non-linear modeling of deep neural networks (DNNs) and their ability to learn beyond pre-Trained environments, this letter proposes a DNN-based feature extraction scheme for the classification of acoustic events. The effectiveness and robustness to noise of the proposed method are demonstrated using a database of indoor surveillance environments.",

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AU - Shin, Minkyu

AU - Shon, Suwon

AU - Kim, Wooil

AU - Han, David K.

AU - Ko, Hanseok

PY - 2017/9

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AB - Recent acoustic event classification research has focused on training suitable filters to represent acoustic events. However, due to limited availability of target event databases and linearity of conventional filters, there is still room for improving performance. By exploiting the non-linear modeling of deep neural networks (DNNs) and their ability to learn beyond pre-Trained environments, this letter proposes a DNN-based feature extraction scheme for the classification of acoustic events. The effectiveness and robustness to noise of the proposed method are demonstrated using a database of indoor surveillance environments.

KW - Acoustic event classification

KW - Acoustic feature

KW - Deep neural network

KW - Transfer learning

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JF - IEICE Transactions on Information and Systems

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DNN transfer learning based non-linear feature extraction for acoustic event classification

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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