Localization and classification of overlapping sound events based on spectrogram-keypoint using acoustic-sensor-network data

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

2 Citations (Scopus)

Abstract

Automatic localization and classification of environmental sound events can provide great aid to many human-centric IoT applications. However as many papers have mentioned, environmental sound events in daily life are complicated and hard to classify especially when multiple sounds happen simultaneously. Unlike most works, which decompose and classify overlapping signals using a unified model, we first decompose overlapping sound signals with a spectrogram-keypoint based localization algorithm. These located and clustered spectrogram-keypoints are subsequently reused for sound source classification. Our major contribution is the modeling of a global cost function to synchronize the time-difference-of-arrivals (TDOA) of each small spectrogram-keypoint and further locating the sound sources by these clustered keypoints. With these clustered keypoints, 2 different classification models are used to classify the sound sources. Our experiments show that our solution is both accurate and low-cost in terms of calculation effort.
Original languageEnglish
Title of host publication2019 IEEE International Conference on Internet of Things and Intelligence System (IoTaIS)
PublisherIEEE
Pages49-55
Number of pages7
ISBN (Electronic)978-1-7281-2516-9
ISBN (Print)978-1-7281-2517-6
DOIs
Publication statusPublished - 6 Feb 2020
EventIEEE International Conference on Internet of Things and Intelligence System, IOTAIS 2019 - Bali, Indonesia
Duration: 5 Nov 20197 Nov 2019
https://iotais.org/iotais-2019/iotais-2019/

Conference

ConferenceIEEE International Conference on Internet of Things and Intelligence System, IOTAIS 2019
Abbreviated titleIOTAIS
CountryIndonesia
CityBali
Period5/11/197/11/19
Internet address

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