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Audio Impairment Recognition using a Correlation-Based Feature Representation
Date Issued
2020-05-28
Date Available
2021-05-26T11:15:50Z
Abstract
Audio impairment recognition is based on finding noise in audio files and categorising the impairment type. Recently, significant performance improvement has been obtained thanks to the usage of advanced deep learning models. However, feature robustness is still an unresolved issue and it is one of the main reasons why we need powerful deep learning architectures. In the presence of a variety of musical styles, handcrafted features are less efficient in capturing audio degradation characteristics and they are prone to failure when recognising audio impairments and could mistakenly learn musical concepts rather than impairment types. In this paper, we propose a new representation of hand-crafted features that is based on the correlation of feature pairs. We experimentally compare the proposed correlation-based feature representation with a typical raw feature representation used in machine learning and we show superior performance in terms of compact feature dimensionality and improved computational speed in the test stage whilst achieving comparable accuracy.
Sponsorship
Science Foundation Ireland
Other Sponsorship
Insight Research Centre
The Engineering and Physical Sciences Research Council
The Alan Turing Institute
Type of Material
Conference Publication
Publisher
IEEE
Copyright (Published Version)
2020 IEEE
Web versions
Language
English
Status of Item
Peer reviewed
Part of
2020 Twelfth International Conference on Quality of Multimedia Experience (QoMEX)
Conference Details
The 2020 Twelfth International Conference on Quality of Multimedia Experience (QoMEX), Athlone, Ireland (held online due to coronavirus outbreak), 26-28 May 2020
ISBN
978-1-7281-5965-2
This item is made available under a Creative Commons License
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