Sound Event Detection in Multichannel Audio Using Spatial and Harmonic Features

Sharath Adavanne, Giambattista Parascandolo, Pasi Pertilä, Toni Heittola, Tuomas Virtanen

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

    Abstract

    In this paper, we propose the use of spatial and harmonic features in combination with long short term memory (LSTM) recurrent neural network (RNN) for automatic sound event detection (SED) task. Real life sound recordings typically have many overlapping sound events, making it hard to recognize with just mono channel audio. Human listeners have been successfully recognizing the mixture of overlapping sound events using pitch cues and exploiting the stereo (multichannel) audio signal available at their ears to spatially localize these events. Traditionally SED systems have only been using mono channel audio, motivated by the human listener we propose to extend them to use multichannel audio. The proposed SED system is compared against the state of the art mono channel method on the development subset of TUT sound events detection 2016 database. The proposed method improves the F-score by 3.75% while reducing the error rate by 6%.
    Original languageEnglish
    Title of host publicationProceedings of the Detection and Classification of Acoustic Scenes and Events 2016 Workshop (DCASE2016)
    PublisherTampere University of Technology. Department of Signal Processing
    Pages6-10
    ISBN (Electronic)978-952-15-3807-0
    Publication statusPublished - 2016
    Publication typeA4 Article in a conference publication
    EventDetection and Classification of Acoustic Scenes and Events Workshop -
    Duration: 1 Jan 2000 → …

    Conference

    ConferenceDetection and Classification of Acoustic Scenes and Events Workshop
    Period1/01/00 → …

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