Multiple Sound Source Localization with SVD-PHAT

Francois Grondin; James Glass

arXiv:1906.11913·eess.AS·July 1, 2019·Interspeech

Multiple Sound Source Localization with SVD-PHAT

Francois Grondin, James Glass

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TL;DR

This paper presents an improved SVD-PHAT method for localizing multiple sound sources more accurately and efficiently, suitable for real-time applications, by projecting observations onto orthogonal subspaces.

Contribution

It introduces a modified phase transform using SVD for better multi-source localization with low computational complexity.

Findings

01

Achieves up to 0.0395 radians reduction in RMSE compared to SRP-PHAT

02

Enhances localization accuracy for multiple sound sources

03

Maintains low algorithm complexity for real-time use

Abstract

This paper introduces a modification of phase transform on singular value decomposition (SVD-PHAT) to localize multiple sound sources. This work aims to improve localization accuracy and keeps the algorithm complexity low for real-time applications. This method relies on multiple scans of the search space, with projection of each low-dimensional observation onto orthogonal subspaces. We show that this method localizes multiple sound sources more accurately than discrete SRP-PHAT, with a reduction in the Root Mean Square Error up to 0.0395 radians.

Tables2

Table 1. Table 1: This is an example of a table

Ratio		Decibels
$1$	$/ 10$	$- 20$
$1$	$/ 1$	$0$
$2$	$/ 1$	$\approx 6$
$3.16$	$/ 1$	$10$
$10$	$/ 1$	$20$
$100$	$/ 1$	$40$
$1000$	$/ 1$	$60$

Table 2. Table 2: Main predefined styles in Word

Style Name	Entities in a Paper
Title	Title
Author	Author name
Affiliation	Author affiliation
Email	Email address
AbstractHeading	Abstract section heading
Body Text	First paragraph in abstract
Body Text Next	Following paragraphs in abstract
Index	Index terms
1. Heading 1	1^st level section heading
1.1 Heading 2	2^nd level section heading
1.1.1 Heading 3	3^rd level section heading
Body Text	First paragraph in section
Body Text Next	Following paragraphs in section
Figure Caption	Figure caption
Table Caption	Table caption
Equation	Equations
• List Bullet	Bulleted lists
[1] Reference	References

Equations8

x (t) = s (f_{ω} (t))

x (t) = s (f_{ω} (t))

f_{ω} (t) = \frac{1}{2 π j} \oint_{C} \frac{ν ^{- 1 k} d ν}{( 1 - β ν ^{- 1} ) ( ν ^{- 1} - β )}

f_{ω} (t) = \frac{1}{2 π j} \oint_{C} \frac{ν ^{- 1 k} d ν}{( 1 - β ν ^{- 1} ) ( ν ^{- 1} - β )}

\oint_{C} F (z) d z = 2 π j k \sum Res [F (z), p_{k}]

\oint_{C} F (z) d z = 2 π j k \sum Res [F (z), p_{k}]

1 + 1 = π

1 + 1 = π

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Full text

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Index Terms: speech recognition, human-computer interaction, computational paralinguistics

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[TABLE]

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[TABLE]

Finally we have proven the secret theorem of all speech sciences. No more math is needed to show how useful the result is!

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3 Discussion

This is the discussion. This is the discussion. This is the discussion. Is there any discussion?

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4 Conclusions

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