Individualizing Head-Related Transfer Functions for Binaural Acoustic Applications

A Head Related Transfer Function (HRTF) characterizes how a hu-man ear receives sounds from a point in space, and depends on the shapes of one's head, pinna, and torso. Accurate estimations of HRTFs for human subjects are crucial in enabling binaural acoustic applications such as sound localiza...

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Published in:2022 21st ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) pp. 105 - 117
Main Authors: Zandi, Navid H., El-Mohandes, Awny M., Zheng, Rong
Format: Conference Proceeding
Language:English
Published: IEEE 01.05.2022
Subjects:
Acoustic applications
Binaural Localization
Conditional Variational AutoEncoder (CVAE)
Data acquisition
Estimation
Head-Related Transfer Function (HRTF)
Location awareness
Neural networks
Sound Spatialization
Torso
Transfer functions
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Abstract A Head Related Transfer Function (HRTF) characterizes how a hu-man ear receives sounds from a point in space, and depends on the shapes of one's head, pinna, and torso. Accurate estimations of HRTFs for human subjects are crucial in enabling binaural acoustic applications such as sound localization and 3D sound spatialization. Unfortunately, conventional approaches for HRTF estimation rely on specialized devices or lengthy measurement processes. This work proposes a novel lightweight method for HRTF individual-ization that can be implemented using commercial-off-the-shelf components and performed by average users in home settings. The proposed method has two key components: a generative neural network model that can be individualized to predict HRTFs of new subjects from sparse measurements, and a lightweight measurement procedure that collects HRTF data from spatial locations. Exten-sive experiments using a public dataset and in house measurement data from 10 subjects of different ages and genders, show that the individualized models significantly outperform a baseline model in the accuracy of predicted HRTFs. To further demonstrate the advantages of individualized HRTFs, we implement two prototype applications for binaural localization and acoustic spatialization. We find that the performance of a localization model is improved by 15° after trained with individualized HRTFs. Furthermore, in hearing tests, the success rate of correctly identifying the azimuth direction of incoming sounds increases by 183% after individualization.
AbstractList A Head Related Transfer Function (HRTF) characterizes how a hu-man ear receives sounds from a point in space, and depends on the shapes of one's head, pinna, and torso. Accurate estimations of HRTFs for human subjects are crucial in enabling binaural acoustic applications such as sound localization and 3D sound spatialization. Unfortunately, conventional approaches for HRTF estimation rely on specialized devices or lengthy measurement processes. This work proposes a novel lightweight method for HRTF individual-ization that can be implemented using commercial-off-the-shelf components and performed by average users in home settings. The proposed method has two key components: a generative neural network model that can be individualized to predict HRTFs of new subjects from sparse measurements, and a lightweight measurement procedure that collects HRTF data from spatial locations. Exten-sive experiments using a public dataset and in house measurement data from 10 subjects of different ages and genders, show that the individualized models significantly outperform a baseline model in the accuracy of predicted HRTFs. To further demonstrate the advantages of individualized HRTFs, we implement two prototype applications for binaural localization and acoustic spatialization. We find that the performance of a localization model is improved by 15° after trained with individualized HRTFs. Furthermore, in hearing tests, the success rate of correctly identifying the azimuth direction of incoming sounds increases by 183% after individualization.
Author Zheng, Rong
El-Mohandes, Awny M.
Zandi, Navid H.
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  givenname: Navid H.
  surname: Zandi
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  givenname: Awny M.
  surname: El-Mohandes
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  organization: Mansoura University,Mansoura,Egypt
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  givenname: Rong
  surname: Zheng
  fullname: Zheng, Rong
  email: rzheng@mcmaster.ca
  organization: McMaster University,Hamilton,Canada
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Snippet A Head Related Transfer Function (HRTF) characterizes how a hu-man ear receives sounds from a point in space, and depends on the shapes of one's head, pinna,...
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StartPage 105
SubjectTerms Acoustic applications
Binaural Localization
Conditional Variational AutoEncoder (CVAE)
Data acquisition
Estimation
Head-Related Transfer Function (HRTF)
Location awareness
Neural networks
Sound Spatialization
Torso
Transfer functions
Title Individualizing Head-Related Transfer Functions for Binaural Acoustic Applications
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