The machine learning approach for analysis of sound scenes and events

    Research output: Chapter in Book/Report/Conference proceedingChapterScientificpeer-review

    10 Citations (Scopus)


    This chapter explains the basic concepts in computational methods used for analysis of sound scenes and events. Even though the analysis tasks in many applications seem different, the underlying computational methods are typically based on the same principles. We explain the commonalities between analysis tasks such as sound event detection, sound scene classification, or audio tagging. We focus on the machine learning approach, where the sound categories (i.e., classes) to be analyzed are defined in advance. We explain the typical components of an analysis system, including signal pre-processing, feature extraction, and pattern classification. We also preset an example system based on multi-label deep neural networks, which has been found to be applicable in many analysis tasks discussed in this book. Finally, we explain the whole processing chain that involves developing computational audio analysis systems. © Springer International Publishing AG 2018. All rights reserved.
    Original languageEnglish
    Title of host publicationComputational Analysis of Sound Scenes and Events
    EditorsTuomas Virtanen, Mark D. Plumbley, Dan Ellis
    Place of PublicationCham
    Number of pages33
    ISBN (Electronic)978-3-319-63450-0
    ISBN (Print)978-3-319-63449-4
    Publication statusPublished - 2018
    Publication typeA3 Book chapter

    Publication forum classification

    • Publication forum level 2


    Dive into the research topics of 'The machine learning approach for analysis of sound scenes and events'. Together they form a unique fingerprint.

    Cite this