Biomimetic Sound Biomimetic Sound Localization Utilizing - - PowerPoint PPT Presentation

Biomimetic Sound Biomimetic Sound Localization Utilizing Localization Utilizing Head Head-Related Transfer Related Transfer Functions Functions

RenJase Engineering RenJase Engineering

• Dr. Vikram Kapila, Project Advisor
• Dr. Vikram Kapila, Project Advisor

Mechatronics Laboratory Mechatronics Laboratory Department of Mechanical Engineering, NYU Polytechnic Department of Mechanical Engineering, NYU Polytechnic Funding Provided by the NSF SMART Program Funding Provided by the NSF SMART Program

Goal Goal

 Our goal is to create:

Our goal is to create:

 an autonomous robot that can,

an autonomous robot that can,

 in a relatively quiet room,

in a relatively quiet room,

 respond to a sound

respond to a sound

 by rotating and elevating its head

by rotating and elevating its head

 to face the sound source within 5 degrees.

to face the sound source within 5 degrees.

 This sound

This sound-localization is to be done using: localization is to be done using:

 Two microphones and

Two microphones and

 Head Related Transfer Functions

Head Related Transfer Functions

Background Background

 Techniques for Locating Sound

Techniques for Locating Sound

 Inter

Inter-Aural Time Delay Aural Time Delay

 Inter

Inter-Aural Volume Difference Aural Volume Difference

 But both result in Front

But both result in Front-Back ambiguities when using Back ambiguities when using

• nly two microphones
• nly two microphones

 Head

Head-Related Transfer Functions Related Transfer Functions

 Have been identified as an important biological strategy

Have been identified as an important biological strategy

 Are potentially distinct for all directions

Are potentially distinct for all directions

 Have been described for humans and other systems

Have been described for humans and other systems

Fourier Transform Fourier Transform

 Fourier Transform (FT)

Fourier Transform (FT)

 Provides frequency content of complicated time

Provides frequency content of complicated time-domain domain signals signals

Transfer Functions Transfer Functions

 Transfer Function

Transfer Function

 Describes the effect of a

Describes the effect of a system on a signal (the system on a signal (the change in magnitude and change in magnitude and phase), such that phase), such that

 Output = TF * Input

Output = TF * Input

 Head

Head-Related Transfer Related Transfer Function (HRTF) = Function (HRTF) =

 Magnitude( FT[Output(t)]

Magnitude( FT[Output(t)] / FT[Input(t)] ) / FT[Input(t)] )

Head Head-Related Transfer Function Related Transfer Function (HRTF) Ratios (HRTF) Ratios [a.k.a “Renjun is a genius.”] [a.k.a “Renjun is a genius.”]

 A listener does not know the Input signals

A listener does not know the Input signals

 Cannot use Transfer Functions to locate sounds

Cannot use Transfer Functions to locate sounds

 Instead we use HRTF Ratios

Instead we use HRTF Ratios

 = TF_Right / TF_Left

= TF_Right / TF_Left

 = (Output_Right/Input) / (Output_Left/Input)

= (Output_Right/Input) / (Output_Left/Input)

 = Output_Right / Output_Left

= Output_Right / Output_Left

 Note: Fourier Transforms have been dropped for brevity

Note: Fourier Transforms have been dropped for brevity

 In this way, the Input is not needed.

In this way, the Input is not needed.

Experimental Setup Experimental Setup

 SigLab

SigLab

 Creates Input signal

Creates Input signal

 Calculates Fourier Transform

Calculates Fourier Transform

 Amplifier

Amplifier

 Sound Chamber

Sound Chamber

 Speaker

Speaker

 Air

Air

 Microphones

Microphones

 Amplifier

Amplifier

 SigLab

SigLab

 Captures Output signal

Captures Output signal

 Calculates Fourier Transform

Calculates Fourier Transform

 MatLab

MatLab

 Builds databases

Builds databases

 Performs analysis

Performs analysis

Building Databases of HRTF Ratios Building Databases of HRTF Ratios

Example: 12 positions (30 deg spacing), 5 data per position Example: 12 positions (30 deg spacing), 5 data per position

Matching Methods Matching Methods (Minimizing Differences) (Minimizing Differences)

 Sound test data is compared to the Database entries

Sound test data is compared to the Database entries

 To find the best match, which

To find the best match, which

 Indicates the position of the test sound

Indicates the position of the test sound

 Least Squares

Least Squares

 Quantifies “sameness” using absolute distances

Quantifies “sameness” using absolute distances

 SUM[ (Test

SUM[ (Test – DatabaseEntry)^2 ] DatabaseEntry)^2 ]

 Least Standard Deviations

Least Standard Deviations

 Quantifies “sameness” using statistical distances

Quantifies “sameness” using statistical distances

 SUM[ abs(# STDs Test is from DatabaseEntryMean) ]

SUM[ abs(# STDs Test is from DatabaseEntryMean) ]

Matching Results Matching Results

 12 Positions

12 Positions

 30 Degree Increments

30 Degree Increments

 Matching Percent Least

Matching Percent Least Squares = 82.67% Squares = 82.67%

 Matching Percent STD =

Matching Percent STD =

98.33% 98.33%

 36 Positions

36 Positions

 10 Degree Increments

10 Degree Increments

 Matching Percent STD =

Matching Percent STD =

99.86% 99.86%

 72 Positions

72 Positions

 5 Degree Increments

5 Degree Increments

 Matching Percent STD=

Matching Percent STD=

100% 100%

O.K., It’s Not Really That Good O.K., It’s Not Really That Good

 When the “half angles” are tested

When the “half angles” are tested

 { 5, 15, 25…} vs. the database of { 0, 10, 20, 30…}

{ 5, 15, 25…} vs. the database of { 0, 10, 20, 30…}

 The rate of matching to a neighboring 10 is only

The rate of matching to a neighboring 10 is only

76.26% 76.26%

 No bueno.

No bueno.

Adjacent Analysis Adjacent Analysis

 We desire a database where each position is most

We desire a database where each position is most similar to its adjacent neighbors. similar to its adjacent neighbors.

 This would allow us to interpolate.

This would allow us to interpolate.

 A human HRTF database created at University of

A human HRTF database created at University of California, Davis has this property. California, Davis has this property.

 Analyzing their data revealed a critical angle at which this

Analyzing their data revealed a critical angle at which this property degrades (somewhere between 10 and 30 degrees). property degrades (somewhere between 10 and 30 degrees).

 Our 360x5 database exhibits this property for only 58%

Our 360x5 database exhibits this property for only 58%

• f the positions (though our database is currently small)
• f the positions (though our database is currently small)

 We have not yet reached the critical angle. Is it > 1?

We have not yet reached the critical angle. Is it > 1?

Future Directions Future Directions

 Phase I:

Phase I: Completed Completed

 Demonstrate concept

Demonstrate concept

 Develop methods

Develop methods

 Phase II: Build a Robot

Phase II: Build a Robot

 Include ability to adjust head elevation

Include ability to adjust head elevation

 Automate database construction in 3D

Automate database construction in 3D

 Create a database with much finer resolution

Create a database with much finer resolution

 Phase III:

Phase III:

 Miniaturize,

Miniaturize,

 Commercialize, and

Commercialize, and

 Get Rich

Get Rich