Connexions

You are here: Home » Content » Results and Discussion on Audio Localization
Content Actions
Lenses

What is a lens?

Lenses

A lens is a custom view of Connexions content. You can think of it as a fancy kind of list that will let you see Connexions through the eyes of organizations and people you trust.

What is in a lens?

Lens makers point to Connexions materials (modules and collections), creating a guide that includes their own comments and descriptive tags about the content.

Who can create a lens?

Any individual Connexions member, a community, or a respected organization.

This content is ...
Affiliated with (?)
This content is either by members of the organizations listed or about topics related to the organizations listed. Click each link to see a list of all content affiliated with the organization.

  • This module is included inLens: Rice University OpenCourseWare
    By: OpenCourseWare ConsortiumAs a part of collection:"Audio Localization"

    Click the "Rice University OCW" link to see all content affiliated with them.

    Rice University OCW
Tags

(?)

These tags come from the endorsement, affiliation, and other lenses that include this content.

Results and Discussion on Audio Localization

Module by: Elizabeth Gregory, Joseph Cole

Summary: In this section, we will discuss the results of both the MATLAB simulation and the DSK and the areas that need improving.

Matlab Simulation Results

In running our simulation, we discovered that while our region output was not always correct, we maintained accuracy within 22.5°. Tha algorithm seemed to have the most difficultly on the edges of the field of view. Our exact results can be found in Table 1 and Table 2.
Without Noise
True Region True Angle Estimated Region
0 π16 16 0
1 3π16 3 16 0
2 5π16 5 16 2
3 7π16 7 16 3
4 9π16 9 16 4
5 11π16 11 16 5
6 13π16 13 16 6
7 15π16 15 16 7
With Noise, SNR=2
True Region True Angle Estimated Region
0 π16 16 1
1 3π16 3 16 2
2 5π16 5 16 2
3 7π16 7 16 2
4 9π16 9 16 6
5 11π16 11 16 7
6 13π16 13 16 6
7 15π16 15 16 6

Results with the DSK

In testing our DSK algorithm, we started with the ideal signal, generated using Matlab. The results had more error than in Matlab, but seemed reasonable at the time. However, in multiple trials of the same ideal signal, the DSK responded differently each time, indicating that our algorithm still needed a bit of work.
When we tried to implement the same algorithm using a real signal, generated from a computer across the room, we received very poor results. In the end, the DSK could tell whether the signal came from the left or the right, but only when the lab was quiet and empty. Also, the program was very sensitive to alternate signal paths and the general acoustics of the room. All in all, our program was not very reliable, as shown in Table 3.
C Results
True Region True Angle Estimated Region
0 π16 16 4
1 3π16 3 16 3
2 5π16 5 16 6
3 7π16 7 16 3
4 9π16 9 16 5
5 11π16 11 16 3
6 13π16 13 16 4
7 15π16 15 16 6

Areas of Improvement

All in all, we need to start the improvement by doing a better job designing and checking the array beampattern. Also, we need a better algorithm for integration. For this algorithm, we need to know how much of a cycle is needed, how integration length affects accuracy, and how to deal with non-periodic signals. Finally, we need a better understanding and control over the room acoustics, as well as more time to fully test the algorithm.

Comments, questions, feedback, criticisms?

Send feedback