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Volume 39 Issue 4
September  2024
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Milling M, Liu S, Triantafyllopoulos A et al. Audio enhancement for computer audition—An iterative training paradigm using sample importance. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 39(4): 895−911 July 2024. DOI: 10.1007/s11390-024-2934-x.
Citation: Milling M, Liu S, Triantafyllopoulos A et al. Audio enhancement for computer audition—An iterative training paradigm using sample importance. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 39(4): 895−911 July 2024. DOI: 10.1007/s11390-024-2934-x.
shu

Audio Enhancement for Computer Audition—An Iterative Training Paradigm Using Sample Importance

  • 1.

    Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Augsburg 86159, Germany

  • 2.

    Chair of Health Informatics, München rechts der Isar, Technical University of Munich, Munich 81675, Germany

  • 3.

    Munich Center for Machine Learning, Munich 80333, Germany

  • 4.

    Huawei Technologies, Munich, Munich 80992, Germany

  • 5.

    Munich Data Science Institute, Garching 85748, Germany

  • 6.

    Group on Language, Audio and Music, Imperial College London, London SW7 2AZ, U.K.

Funds: This research was partly supported by the Affective Computing & HCI Innovation Research Lab between Huawei Technologies and the University of Augsburg, and the EU H2020 Project under Grant No. 101135556 (INDUX-R).
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  • Author Bio:

    Manuel Milling received his Bachelor of Science in physics and in computer science from the University of Augsburg, Augsburg, in 2014 and 2015, respectively, and his Master of Science in physics from the same university in 2018. He is currently a Ph.D. candidate in computer science at the chair of Health Informatics, Technical University of Munich, Munich. His research interests include machine learning with a particular focus on the core understandings of and applications of deep learning methodologies

    Shuo Liu received his Bachelor degree from the Nanjing University of Posts and Telecommunications, Nanjing, in 2012, and his M.Sc. degree from the Technical University of Darmstadt, Darmstadt, in 2017. He worked as a researcher in the Sivantos group for hearing aids solutions. He is currently a Ph.D. candidate at the Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Augsburg. His research focuses are deep learning for audio processing, mobile computing, digital health, and affective computing

    Andreas Triantafyllopoulos received his diploma in ECE from the University of Patras, Patras, in 2017. He is working toward his doctoral degree with the Chair of Health Informatics, Technical University of Munich, Munich. His current focus is on deep learning methods for auditory intelligence and affective computing

    Ilhan Aslan received his diploma in 2004 from Saarland University, Saarland, and his doctoral degree in 2014 at the Center for HCI from Paris-Lodron University Salzburg in Austria. He was an akad. Rat (assistant professor) at University of Augsburg, Augsburg, from 2016 onward before joining Huawei Technologies, Munich, in 2020 as an HCI Expert where he leads an HCI research team and managed the Affective Computing & HCI Innovation Research Lab. His research focus is at the intersection of HCI, IxD, and affective computing, exploring the future of human-centred multimedia and multimodal interaction

    Björn W. Schuller: Björn W Schuller received his diploma in 1999, his doctoral degree in 2006, and his habilitation and was entitled Adjunct Teaching Professor in 2012 all in electrical engineering and information technology from Technical University of Munich in Munich. He is full professor of artificial intelligence and the Head of GLAM at Imperial College London, Chair of the Chair for Health Informatics, MRI, Technical University of Munich, Munich, amongst other Professorships and Affiliations. He is a fellow of IEEE and Golden Core Awardee of the IEEE Computer Society, Fellow of the ACM, Fellow and President-Emeritus of the AAAC, Fellow of the BCS, Fellow of the ELLIS, Fellow of the ISCA, and Elected Full Member Sigma Xi. He (co-)authored 1400+ publications (60000+ citations, h-index=110)

  • Received Date: October 25, 2022
  • Accepted Date: June 29, 2024
    Abstract

  • Neural network models for audio tasks, such as automatic speech recognition (ASR) and acoustic scene classification (ASC), are susceptible to noise contamination for real-life applications. To improve audio quality, an enhancement module, which can be developed independently, is explicitly used at the front-end of the target audio applications. In this paper, we present an end-to-end learning solution to jointly optimise the models for audio enhancement (AE) and the subsequent applications. To guide the optimisation of the AE module towards a target application, and especially to overcome difficult samples, we make use of the sample-wise performance measure as an indication of sample importance. In experiments, we consider four representative applications to evaluate our training paradigm, i.e., ASR, speech command recognition (SCR), speech emotion recognition (SER), and ASC. These applications are associated with speech and non-speech tasks concerning semantic and non-semantic features, transient and global information, and the experimental results indicate that our proposed approach can considerably boost the noise robustness of the models, especially at low signal-to-noise ratios, for a wide range of computer audition tasks in everyday-life noisy environments.

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