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Re: [AUDITORY] Seeking advice on using ANF firing rate to reslove front-back confusion in sound localization model

Dear Prof. Jan Schnupp,


I really appreciate your very timely response, and I apologize for my delayed reply.

I would like to clarify my current research activities and explain why I am exploring the use of ANF and MSO firing rates for localization tasks. Perhaps I did not articulate my intentions clearly in my previous email.

I I am developing a localization model which is applicable for both normal hearing and hearing impaired. So I choose to use Zilany's model instead of using a gammatone filter. From this model I can get ANF firing rate. 

Furthermore, I am employing another model to simulate the MSO, using HRTFs convolved with a sine tone as input. This model reveals a nonlinear relationship between azimuth/elevation angles and the averaged MSO firing rates for each characteristic frequency, as illustrated below. Using regression methods, I attempt to predict the angles from the MSO firing rates. However, this approach presents a notable challenge: the front-back confusion is evident. To address this, I am exploring alternative cues or utilizing the ANF firing rate. Unfortunately, leveraging ANF firing rate information across multiple frequencies has not yielded success thus far.





The MSO model I am using, detailed in https://doi.org/10.3389/fnins.2018.00140, predicts ITD from the MSO, and I am considering applying a similar method to predict azimuths.

Yes, I also have this up-down confusion when I tried to predict the elevation in range of -90-270, as shown below.




Could you possibly suggest any references or strategies to help resolve these confusions by incorporating the shape of these confusion cones?

Thanks in advance,

Warm regards,


Qin



From: Jan Schnupp <jan.schnupp@xxxxxxxxxxxxxx>
Sent: Wednesday, 26 February 2025 07:26:43
To: Qin Liu
Cc: AUDITORY@xxxxxxxxxxxxxxx
Subject: Re: [AUDITORY] Seeking advice on using ANF firing rate to reslove front-back confusion in sound localization model
 
Dear Qin,

I do not think your problem is solvable without some kind of assumptions about HRTF and/or cross-frequency integration.
I am also confused about the fact that you are looking ant ANF rates and MSO, as MSO is usually thought of as a structure interested in fine timing rather than temporally coarse rates. 
And presumably, if your current solution can't do front-back, then I assume you can't do top and bottom either?
In any event, whether you look at either ILDs or ITDs in any one frequency band, these will only narrow down source directions to one specific cone of confusion.
And you cannot do better unless you incorporate at least some knowledge or assumption about what the shapes of these cones are and how they differ for each cue and frequency band, and combine info across frequencies accordingly, in a sense by discounting directions where the solution cones do not intersect and favoring directions where they do. 

Jan


---------------------------------------
Prof Jan Schnupp
Gerald Choa Neuroscience Institute
The Chinese University of Hong Kong
Sha Tin
Hong Kong


On Wed, 26 Feb 2025 at 13:23, Qin Liu <000003c563e12bd3-dmarc-request@xxxxxxxxxxxxxxx> wrote:

Dear auditory list,


I am currently working on a project involving sound localization using firing rates from auditory nerve fibers (ANFs) and the medial superior olive (MSO). However, I have encountered an issue: I am unable to distinguish between front and back sound sources using MSO firing rates alone but only the left-right.

I am considering whether auditory nerve fiber (ANF) firing rates might provide a solution, but I am uncertain how to utilize them effectively. For instance, I have experimented with analyzing the positive gradients of ANF firing rates but have not yet achieved meaningful results.

Could anyone suggest an auditory metric derived from binaural signals, ANF firing rates, or MSO that could classify front/back sources without relying on HRTF template matching? Any insights or alternative approaches would be invaluable to my work.

Thank you in advance. I sincerely appreciate any guidance you can offer.

Best regards,

Qin Liu
Doctoral Student
Laboratory of Wave Engineering, École Polytechnique Fédérale de Lausanne (EPFL)
Email: qin.liu@xxxxxxx