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Intelligent Audio Signal Processing – Do We Still Need Annotated Datasets?

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Intelligent Information and Database Systems (ACIIDS 2022)

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Abstract

In this paper, intelligent audio signal processing examples are shortly described. The focus is, however, on the machine learning approach and datasets needed, especially for deep learning models. Years of intense research produced many important results in this area; however, the goal of fully intelligent signal processing, characterized by its autonomous acting, is not yet achieved. Therefore, a review of state-of-the-art concerning this area is given. The aspect of showing the importance of acquiring an appropriate dataset containing audio samples dedicated to the task is also shown. The paper starts with samples of audio-related datasets resulting from the search engine inquiry. Then, examples of research studies along with results are given. Also, several works carried out by the author and her collaborators are presented. Some thoughts on future work are included with answering a question of whether annotated datasets are still needed.

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Authors and Affiliations

  1. Gdansk University of Technology, G. Narutowicza 11/12, 81-230, Gdansk, Poland

    Bozena Kostek

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  1. Bozena Kostek
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Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Vietnam National University, Ho Chi Minh City, Ho Chi Minh City, Vietnam

    Tien Khoa Tran

  3. Al-Farabi Kazakh National University, Almaty, Kazakhstan

    Ualsher Tukayev

  4. National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

  5. Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

  6. University of Newcastle, Newcastle, NSW, Australia

    Edward Szczerbicki

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Kostek, B. (2022). Intelligent Audio Signal Processing – Do We Still Need Annotated Datasets?. In: Nguyen, N.T., Tran, T.K., Tukayev, U., Hong, TP., Trawiński, B., Szczerbicki, E. (eds) Intelligent Information and Database Systems. ACIIDS 2022. Lecture Notes in Computer Science(), vol 13758. Springer, Cham. https://doi.org/10.1007/978-3-031-21967-2_55

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