Multi Target Acoustic Source Tracking using Track Before Detect

Maurice Fallon; S. Godsill

doi:10.1109/ASPAA.2007.4392977

Multi Target Acoustic Source Tracking using Track Before Detect

Maurice Fallon, S. Godsill

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

Abstract

Particle Filter-based Source Localisation algorithms attempt to track the position of a sound source - a person speaking in a room - based on the current data from a distributed microphone array as well as all previous data up to that point. This paper introduces a multi-target methodology for acoustic source tracking. The methodology is based upon the Track Before Detect (TBD) framework. The algorithm also implicitly evaluates the source activity using a variable appended to the state vector. Examples of typical tracking performance are given using a set of real speech recordings with two sources active simultaneously.

Original language	English
Title of host publication	Applications of Signal Processing to Audio and Acoustics, 2007 IEEE Workshop on
Publisher	Institute of Electrical and Electronics Engineers
Pages	102-105
Number of pages	4
ISBN (Electronic)	978-1-4244-1619-6
ISBN (Print)	978-1-4244-1620-2
DOIs	https://doi.org/10.1109/ASPAA.2007.4392977
Publication status	Published - 1 Oct 2007

Keywords / Materials (for Non-textual outputs)

Acoustic applications
Acoustic signal detection
Acoustic signal processing
Conferences
Frequency
Microphone arrays
Particle tracking
Signal processing algorithms
Speech
Target tracking

Access to Document

10.1109/ASPAA.2007.4392977

07_fallon_waspaaAccepted author manuscript, 163 KB

http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4392977

Cite this

@inproceedings{5017f51c6a9a4864893de294310e6974,

title = "Multi Target Acoustic Source Tracking using Track Before Detect",

abstract = "Particle Filter-based Source Localisation algorithms attempt to track the position of a sound source - a person speaking in a room - based on the current data from a distributed microphone array as well as all previous data up to that point. This paper introduces a multi-target methodology for acoustic source tracking. The methodology is based upon the Track Before Detect (TBD) framework. The algorithm also implicitly evaluates the source activity using a variable appended to the state vector. Examples of typical tracking performance are given using a set of real speech recordings with two sources active simultaneously.",

keywords = "Acoustic applications, Acoustic signal detection, Acoustic signal processing, Conferences, Frequency, Microphone arrays, Particle tracking, Signal processing algorithms, Speech, Target tracking",

author = "Maurice Fallon and S. Godsill",

year = "2007",

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day = "1",

doi = "10.1109/ASPAA.2007.4392977",

language = "English",

isbn = "978-1-4244-1620-2",

pages = "102--105",

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publisher = "Institute of Electrical and Electronics Engineers",

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TY - GEN

T1 - Multi Target Acoustic Source Tracking using Track Before Detect

AU - Fallon, Maurice

AU - Godsill, S.

PY - 2007/10/1

Y1 - 2007/10/1

N2 - Particle Filter-based Source Localisation algorithms attempt to track the position of a sound source - a person speaking in a room - based on the current data from a distributed microphone array as well as all previous data up to that point. This paper introduces a multi-target methodology for acoustic source tracking. The methodology is based upon the Track Before Detect (TBD) framework. The algorithm also implicitly evaluates the source activity using a variable appended to the state vector. Examples of typical tracking performance are given using a set of real speech recordings with two sources active simultaneously.

AB - Particle Filter-based Source Localisation algorithms attempt to track the position of a sound source - a person speaking in a room - based on the current data from a distributed microphone array as well as all previous data up to that point. This paper introduces a multi-target methodology for acoustic source tracking. The methodology is based upon the Track Before Detect (TBD) framework. The algorithm also implicitly evaluates the source activity using a variable appended to the state vector. Examples of typical tracking performance are given using a set of real speech recordings with two sources active simultaneously.

KW - Acoustic applications

KW - Acoustic signal detection

KW - Acoustic signal processing

KW - Conferences

KW - Frequency

KW - Microphone arrays

KW - Particle tracking

KW - Signal processing algorithms

KW - Speech

KW - Target tracking

U2 - 10.1109/ASPAA.2007.4392977

DO - 10.1109/ASPAA.2007.4392977

M3 - Conference contribution

SN - 978-1-4244-1620-2

SP - 102

EP - 105

BT - Applications of Signal Processing to Audio and Acoustics, 2007 IEEE Workshop on

PB - Institute of Electrical and Electronics Engineers

ER -

Multi Target Acoustic Source Tracking using Track Before Detect

Abstract

Keywords / Materials (for Non-textual outputs)

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Cite this