ALARM SOUND DETECTION USING TOPOLOGICAL SIGNAL PROCESSING

Tomer Fireaizen; Saar Ron; Omer Bobrowski

doi:10.1109/ICASSP43922.2022.9747228

ALARM SOUND DETECTION USING TOPOLOGICAL SIGNAL PROCESSING

Tomer Fireaizen, Saar Ron, Omer Bobrowski

Technion - Israel Institute of Technology

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

Abstract

We present a novel approach to alarm sound detection using topological data analysis. Our main focus is on proposing a new set of robust features, based on algebraic topology, that are aimed at capturing global structural information about the dynamical system underlying each input signal. In short, we convert each signal into a point cloud and compute its corresponding persistent homology, from which we can extract a variety of useful numerical features. We demonstrate the power of this framework using the UrbanSound8K dataset and show that, by combining topological features with a classical classification method, we achieve state-of-the-art results.

Original language	English
Title of host publication	47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022
Pages	211-215
Number of pages	5
DOIs	https://doi.org/10.1109/ICASSP43922.2022.9747228
State	Published - 2022
Event	47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Virtual, Online, Singapore Duration: 23 May 2022 → 27 May 2022

Conference

Conference	47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022
Country/Territory	Singapore
City	Virtual, Online
Period	23/05/22 → 27/05/22

Keywords

alarm detection
persistent homology
signal classification
topological data analysis
topological signal processing

All Science Journal Classification (ASJC) codes

Software
Signal Processing
Electrical and Electronic Engineering

Access to Document

10.1109/ICASSP43922.2022.9747228

Cite this

@inproceedings{25732470f17e4da6addbe1697cac22a8,

title = "ALARM SOUND DETECTION USING TOPOLOGICAL SIGNAL PROCESSING",

abstract = "We present a novel approach to alarm sound detection using topological data analysis. Our main focus is on proposing a new set of robust features, based on algebraic topology, that are aimed at capturing global structural information about the dynamical system underlying each input signal. In short, we convert each signal into a point cloud and compute its corresponding persistent homology, from which we can extract a variety of useful numerical features. We demonstrate the power of this framework using the UrbanSound8K dataset and show that, by combining topological features with a classical classification method, we achieve state-of-the-art results.",

keywords = "alarm detection, persistent homology, signal classification, topological data analysis, topological signal processing",

author = "Tomer Fireaizen and Saar Ron and Omer Bobrowski",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE; 47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 ; Conference date: 23-05-2022 Through 27-05-2022",

year = "2022",

doi = "10.1109/ICASSP43922.2022.9747228",

language = "الإنجليزيّة",

pages = "211--215",

booktitle = "47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022",

}

Fireaizen, T, Ron, S & Bobrowski, O 2022, ALARM SOUND DETECTION USING TOPOLOGICAL SIGNAL PROCESSING. in 47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022. pp. 211-215, 47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022, Virtual, Online, Singapore, 23/05/22. https://doi.org/10.1109/ICASSP43922.2022.9747228

TY - GEN

T1 - ALARM SOUND DETECTION USING TOPOLOGICAL SIGNAL PROCESSING

AU - Fireaizen, Tomer

AU - Ron, Saar

AU - Bobrowski, Omer

PY - 2022

Y1 - 2022

N2 - We present a novel approach to alarm sound detection using topological data analysis. Our main focus is on proposing a new set of robust features, based on algebraic topology, that are aimed at capturing global structural information about the dynamical system underlying each input signal. In short, we convert each signal into a point cloud and compute its corresponding persistent homology, from which we can extract a variety of useful numerical features. We demonstrate the power of this framework using the UrbanSound8K dataset and show that, by combining topological features with a classical classification method, we achieve state-of-the-art results.

AB - We present a novel approach to alarm sound detection using topological data analysis. Our main focus is on proposing a new set of robust features, based on algebraic topology, that are aimed at capturing global structural information about the dynamical system underlying each input signal. In short, we convert each signal into a point cloud and compute its corresponding persistent homology, from which we can extract a variety of useful numerical features. We demonstrate the power of this framework using the UrbanSound8K dataset and show that, by combining topological features with a classical classification method, we achieve state-of-the-art results.

KW - alarm detection

KW - persistent homology

KW - signal classification

KW - topological data analysis

KW - topological signal processing

UR - http://www.scopus.com/inward/record.url?scp=85131229708&partnerID=8YFLogxK

U2 - 10.1109/ICASSP43922.2022.9747228

DO - 10.1109/ICASSP43922.2022.9747228

M3 - منشور من مؤتمر

SP - 211

EP - 215

BT - 47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022

T2 - 47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022

Y2 - 23 May 2022 through 27 May 2022

ER -

ALARM SOUND DETECTION USING TOPOLOGICAL SIGNAL PROCESSING

Abstract

Conference

Keywords

All Science Journal Classification (ASJC) codes

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