Audio Impairment Recognition using a Correlation-Based Feature Representation

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Title: Audio Impairment Recognition using a Correlation-Based Feature Representation
Authors: Ragano, AlessandroBenetos, EmmanouilHines, Andrew
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Date: 28-May-2020
Online since: 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.
Funding Details: Science Foundation Ireland
Funding Details: 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
Keywords: Machine learning & statisticsAudio impairmentsFeature representationFeature dimensionalityFeature robustnessConvolutional neural networks
DOI: 10.1109/QoMEX48832.2020.9123111
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Language: en
Status of Item: Peer reviewed
Is 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
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Appears in Collections:Computer Science Research Collection
Insight Research Collection

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