Audio-based Event Recognition System for Smart Homes

Anastasios Vafeiadis; Konstantinos Votis; Dimitrios Giakoumis; Dimitrios Tzovaras; Liming Chen; Raouf Hamzaoui

doi:10.1109/UIC-ATC.2017.8397489

Audio-based Event Recognition System for Smart Homes

Anastasios Vafeiadis
, Konstantinos Votis
, Dimitrios Giakoumis
, Dimitrios Tzovaras
, Liming Chen
, Raouf Hamzaoui

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

13 Citations (Scopus)

Abstract

Building an acoustic-based event recognition system for smart homes is a challenging task due to the lack of high-level structures in environmental sounds. In particular, the selection of effective features is still an open problem. We make an important step toward this goal by showing that the combination of Mel-Frequency Cepstral Coefficients, Zero-Crossing Rate, and Discrete Wavelet Transform features can achieve an F1 score of 96.5% and a recognition accuracy of 97.8% with a gradient boosting classifier for ambient sounds recorded in a kitchen environment.

Original language	English
Title of host publication	Audio-based event recognition system for smart homes
Place of Publication	San Francisco, CA, USA
Publisher	IEEE Xplore
Pages	1-8
Number of pages	8
ISBN (Electronic)	978-1-5386-0435-9
ISBN (Print)	978-1-5386-1591-1
DOIs	https://doi.org/10.1109/UIC-ATC.2017.8397489
Publication status	Published (in print/issue) - 8 Aug 2017
Event	2017 IEEE Smart World, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation - San Francisco, CA Duration: 4 Aug 2017 → 8 Aug 2017

Conference

Conference	2017 IEEE Smart World, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation
Period	4/08/17 → 8/08/17

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

feature extraction
mel frequency cepstral coefficient
discrete wavelet tranforms
home computing
signal classificatioin
smart homes
assisted living
activity recognition
audio feature extraction
classification
mel-frequency
zero-crossing rate
wavelets

Access to Document

10.1109/UIC-ATC.2017.8397489

https://ieeexplore.ieee.org/document/8397489/

Cite this

@inproceedings{ffa60e0142f4492da7aad3b11bda1650,

title = "Audio-based Event Recognition System for Smart Homes",

abstract = "Building an acoustic-based event recognition system for smart homes is a challenging task due to the lack of high-level structures in environmental sounds. In particular, the selection of effective features is still an open problem. We make an important step toward this goal by showing that the combination of Mel-Frequency Cepstral Coefficients, Zero-Crossing Rate, and Discrete Wavelet Transform features can achieve an F1 score of 96.5\% and a recognition accuracy of 97.8\% with a gradient boosting classifier for ambient sounds recorded in a kitchen environment.",

keywords = "feature extraction, mel frequency cepstral coefficient, discrete wavelet tranforms, home computing, signal classificatioin, smart homes, assisted living, activity recognition, audio feature extraction, classification, mel-frequency, zero-crossing rate, wavelets",

author = "Anastasios Vafeiadis and Konstantinos Votis and Dimitrios Giakoumis and Dimitrios Tzovaras and Liming Chen and Raouf Hamzaoui",

year = "2017",

month = aug,

day = "8",

doi = "10.1109/UIC-ATC.2017.8397489",

language = "English",

isbn = "978-1-5386-1591-1",

pages = "1--8",

booktitle = "Audio-based event recognition system for smart homes",

publisher = "IEEE Xplore",

address = "United States",

note = "2017 IEEE Smart World, Ubiquitous Intelligence \& Computing, Advanced \& Trusted Computed, Scalable Computing \& Communications, Cloud \& Big Data Computing, Internet of People and Smart City Innovation ; Conference date: 04-08-2017 Through 08-08-2017",

}

Vafeiadis, A, Votis, K, Giakoumis, D, Tzovaras, D, Chen, L & Hamzaoui, R 2017, Audio-based Event Recognition System for Smart Homes. in Audio-based event recognition system for smart homes. IEEE Xplore, San Francisco, CA, USA, pp. 1-8, 2017 IEEE Smart World, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation, 4/08/17. https://doi.org/10.1109/UIC-ATC.2017.8397489

TY - GEN

T1 - Audio-based Event Recognition System for Smart Homes

AU - Vafeiadis, Anastasios

AU - Votis, Konstantinos

AU - Giakoumis, Dimitrios

AU - Tzovaras, Dimitrios

AU - Chen, Liming

AU - Hamzaoui, Raouf

PY - 2017/8/8

Y1 - 2017/8/8

N2 - Building an acoustic-based event recognition system for smart homes is a challenging task due to the lack of high-level structures in environmental sounds. In particular, the selection of effective features is still an open problem. We make an important step toward this goal by showing that the combination of Mel-Frequency Cepstral Coefficients, Zero-Crossing Rate, and Discrete Wavelet Transform features can achieve an F1 score of 96.5% and a recognition accuracy of 97.8% with a gradient boosting classifier for ambient sounds recorded in a kitchen environment.

AB - Building an acoustic-based event recognition system for smart homes is a challenging task due to the lack of high-level structures in environmental sounds. In particular, the selection of effective features is still an open problem. We make an important step toward this goal by showing that the combination of Mel-Frequency Cepstral Coefficients, Zero-Crossing Rate, and Discrete Wavelet Transform features can achieve an F1 score of 96.5% and a recognition accuracy of 97.8% with a gradient boosting classifier for ambient sounds recorded in a kitchen environment.

KW - feature extraction

KW - mel frequency cepstral coefficient

KW - discrete wavelet tranforms

KW - home computing

KW - signal classificatioin

KW - smart homes

KW - assisted living

KW - activity recognition

KW - audio feature extraction

KW - classification

KW - mel-frequency

KW - zero-crossing rate

KW - wavelets

U2 - 10.1109/UIC-ATC.2017.8397489

DO - 10.1109/UIC-ATC.2017.8397489

M3 - Conference contribution

SN - 978-1-5386-1591-1

SP - 1

EP - 8

BT - Audio-based event recognition system for smart homes

PB - IEEE Xplore

CY - San Francisco, CA, USA

T2 - 2017 IEEE Smart World, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation

Y2 - 4 August 2017 through 8 August 2017

ER -

Audio-based Event Recognition System for Smart Homes

Abstract

Conference

UN SDGs

Keywords

Access to Document

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Cite this