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Publication . Article . 2022

Goal-driven, neurobiological-inspired convolutional neural network models of human spatial hearing

Kiki van der Heijden; Siamak Mehrkanoon;
Open Access
Published: 22 Jan 2022 Journal: Neurocomputing, volume 470, pages 432-442 (issn: 0925-2312, Copyright policy )
Country: Netherlands
The human brain effortlessly solves the complex computational task of sound localization using a mixture of spatial cues. How the brain performs this task in naturalistic listening environments (e.g. with reverberation) is not well understood. In the present paper, we build on the success of deep neural networks at solving complex and high-dimensional problems [1] to develop goal-driven, neurobiological-inspired convolutional neural network (CNN) models of human spatial hearing. After training, we visualize and quantify feature representations in intermediate layers to gain insights into the representational mechanisms underlying sound location encoding in CNNs. Our results show that neurobiological-inspired CNN models trained on real-life sounds spatialized with human binaural hearing characteristics can accurately predict sound location in the horizontal plane. CNN localization acuity across the azimuth resembles human sound localization acuity, but CNN models outperform human sound localization in the back. Training models with different objective functions - that is, minimizing either Euclidean or angular distance - modulates localization acuity in particular ways. Moreover, different implementations of binaural integration result in unique patterns of localization errors that resemble behavioral observations in humans. Finally, feature representations reveal a gradient of spatial selectivity across network layers, starting with broad spatial representations in early layers and progressing to sparse, highly selective spatial representations in deeper layers. In sum, our results show that neurobiological-inspired CNNs are a valid approach to modeling human spatial hearing. This work paves the way for future studies combining neural network models with empirical measurements of neural activity to unravel the complex computational mechanisms underlying neural sound location encoding in the human auditory pathway.
Subjects by Vocabulary

Microsoft Academic Graph classification: Artificial neural network Reverberation Feature (computer vision) Encoding (memory) Pattern recognition Convolutional neural network Sound localization Artificial intelligence business.industry business Binaural recording Computer science Angular distance


Convolutional neural network, Human sound localization, Binaural integration, Deep learning, SOUND-LOCALIZATION, LOCATION, ROBUST, Artificial Intelligence, Cognitive Neuroscience, Computer Science Applications

Funded by
Representational Mechanisms of Neural Location Encoding of Real-life Sounds in Normal and Hearing Impaired Listeners.
  • Funder: European Commission (EC)
  • Project Code: 898134
  • Funding stream: H2020 | MSCA-IF-GF
Validated by funder
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Article . 2022
Providers: NARCIS