research-article

Using Smartphones to Classify Urban Sounds

Authors:

Elsa Ferreira Gomes,

Fábio Batista,

Alípio M. JorgeAuthors Info & Claims

C3S2E '16: Proceedings of the Ninth International C* Conference on Computer Science & Software Engineering

Pages 67 - 72

https://doi.org/10.1145/2948992.2949002

Published: 20 July 2016 Publication History

Abstract

The aim of this work is to develop an application for Android able to classifying urban sounds in a real life context. It also enables the collection and classification of new sounds. To train our classifier we use the UrbanSound8K data set available online. We have used a hybrid approach to obtain features, by combining SAX-based multiresolution motif discovery with Mel-Frequency Cepstral Coefficients (MFCC). We also describe different configurations of motif discovery for defining attributes and compare the use of Random Forest and SVM algorithms on this kind of data.

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Cited By

Cardaioli MConti MRavindranath A(2022)For Your Voice Only: Exploiting Side Channels in Voice Messaging for Environment DetectionComputer Security – ESORICS 202210.1007/978-3-031-17143-7_29(595-613)Online publication date: 24-Sep-2022
https://doi.org/10.1007/978-3-031-17143-7_29
Pires IMarques GGarcia NPombo NFlórez-Revuelta FSpinsante STeixeira MZdravevski E(2019)Recognition of Activities of Daily Living and Environments Using Acoustic Sensors Embedded on Mobile DevicesElectronics10.3390/electronics81214998:12(1499)Online publication date: 7-Dec-2019
https://doi.org/10.3390/electronics8121499
Chandrakala SJayalakshmi S(2019)Environmental Audio Scene and Sound Event Recognition for Autonomous SurveillanceACM Computing Surveys10.1145/332224052:3(1-34)Online publication date: 18-Jun-2019
https://dl.acm.org/doi/10.1145/3322240

Using Smartphones to Classify Urban Sounds
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning

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cover image ACM Other conferences

C3S2E '16: Proceedings of the Ninth International C* Conference on Computer Science & Software Engineering

July 2016

152 pages

ISBN:9781450340755

DOI:10.1145/2948992

Editor:
Evan Desai
ConfSys.org
,
General Chair:
Bipin C. Desai
Concordia University, Canada
,
Program Chairs:
Ana Alameida
ISEP
,
Jorge Bernardino
ISEC

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 20 July 2016

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C3S2E '16

C3S2E '16: Ninth International C* Conference on Computer Science & Software Engineering

July 20 - 22, 2016

Porto, Portugal

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Overall Acceptance Rate 12 of 42 submissions, 29%

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Cited By

Cardaioli MConti MRavindranath A(2022)For Your Voice Only: Exploiting Side Channels in Voice Messaging for Environment DetectionComputer Security – ESORICS 202210.1007/978-3-031-17143-7_29(595-613)Online publication date: 24-Sep-2022
https://doi.org/10.1007/978-3-031-17143-7_29
Pires IMarques GGarcia NPombo NFlórez-Revuelta FSpinsante STeixeira MZdravevski E(2019)Recognition of Activities of Daily Living and Environments Using Acoustic Sensors Embedded on Mobile DevicesElectronics10.3390/electronics81214998:12(1499)Online publication date: 7-Dec-2019
https://doi.org/10.3390/electronics8121499
Chandrakala SJayalakshmi S(2019)Environmental Audio Scene and Sound Event Recognition for Autonomous SurveillanceACM Computing Surveys10.1145/332224052:3(1-34)Online publication date: 18-Jun-2019
https://dl.acm.org/doi/10.1145/3322240

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