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Published, Version of Record (VoR)CC BY V4.0, Open
Journal article
Feedback Module Based Convolution Neural Networks for Sound Event Classification
IEEE access, v 9, pp 150993-151003
01 Jan 2021
Abstract
Sound event classification is starting to receive a lot of attention over the recent years in the field of audio processing because of open datasets, which are recorded in various conditions, and the introduction of challenges. To use the sound event classification model in the wild, it is needed to be independent of recording conditions. Therefore, a more generalized model, that can be trained and tested in various recording conditions, must be researched. This paper presents a deep neural network with a dual-path frequency residual network and feedback modules for sound event classification. Most deep neural network based approaches for sound event classification use feed-forward models and train with a single classification result. Although these methods are simple to implement and deliver reasonable results, the integration of recurrent inference based methods has shown potential for classification and generalization performance improvements. We propose a weighted recurrent inference based model by employing cascading feedback modules for sound event classification. In our experiments, it is shown that the proposed method outperforms traditional approaches in indoor and outdoor conditions by 1.94% and 3.26%, respectively.
Metrics
Details
- Title
- Feedback Module Based Convolution Neural Networks for Sound Event Classification
- Creators
- Gwantae Kim - Korea UniversityDavid K. Han - Drexel UniversityHanseok Ko - Korea University
- Publication Details
- IEEE access, v 9, pp 150993-151003
- Publisher
- IEEE
- Number of pages
- 11
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:000717754400001
- Scopus ID
- 2-s2.0-85119615276
- Other Identifier
- 991019168634704721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Information Systems
- Engineering, Electrical & Electronic
- Telecommunications