Conference proceeding
DEEP NEURAL NETWORK BASED LEARNING AND TRANSFERRING MID-LEVEL AUDIO FEATURES FOR ACOUSTIC SCENE CLASSIFICATION
2017 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), pp 796-800
Mar 2017
Abstract
Deep Neural Network (DNN) based transfer learning has been shown to be effective in Visual Object Classification (VOC) for complementing the deficit of target domain training samples by adapting classifiers that have been pre-trained for other large-scaled DataBase (DB). Although there exists an abundance of acoustic data, it can also be said that datasets of specific acoustic scenes are sparse for training Acoustic Scene Classification (ASC) models. By exploiting VOC DNN's ability of learning beyond its pre-trained environments, this paper proposes DNN based transfer learning for ASC. Effectiveness of the proposed method is demonstrated on the database of IEEE DCASE Challenge 2016 Task 1 and home surveillance environment via representative experiments. Its improved performance is verified by comparing it to prominent conventional methods.
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Details
- Title
- DEEP NEURAL NETWORK BASED LEARNING AND TRANSFERRING MID-LEVEL AUDIO FEATURES FOR ACOUSTIC SCENE CLASSIFICATION
- Creators
- Seongkyu Mun - Korea UniversitySuwon Shon - Korea UniversityWooil Kim - Incheon National UniversityDavid K. Han - Office of Naval ResearchHanseok Ko - Korea University
- Publication Details
- 2017 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), pp 796-800
- Series
- International Conference on Acoustics Speech and Signal Processing ICASSP
- Publisher
- IEEE
- Number of pages
- 5
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:000414286200160
- Scopus ID
- 2-s2.0-85023778381
- Other Identifier
- 991021931084804721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Acoustics
- Engineering, Electrical & Electronic