Replicating Human Sound Localization with a Multi-Layer Perceptron Article Swipe
Eric Michael Sumner
,
Rúnar Unnþórsson
,
Morris Riedel
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.5281/zenodo.6797854
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.5281/zenodo.6797854
One of the key capabilities of the human sense of hearing is to determine the direction from which a sound is emanating, a task known as localization. This paper describes the derivation of a machine learning model which performs the same localization task: Given an audio waveform which arrives at the listener’s eardrum, determine the direction of the audio source. Head-related transfer functions (HRTFs) from the ITA-HRTF database of 48 individuals are used to train and validate this model. A series of waveforms is generated from each HRTF, representing the sound pressure level at the listener’s eardrums for various source directions. A feature vector is calculated for each waveform from acoustical properties motivated by prior literature on sound localization; these feature vectors are used to train multi-layer perceptrons (MLPs), a form of artificial neural network, to replicate the behavior of single individuals. Data from three individuals are used to optimize hyperparameters of both the feature extraction and MLP stages for model accuracy. These hyperparameters are then validated by training and analyzing models for all 48 individuals in the database. The errors produced by each model fall in a log-normal distribution. The median model is capable of identifying, with 95% confidence, the sound source direction to within 20 degrees. This result is comparable to previously-reported human capabilities and thus shows that an MLP can successfully replicate the human sense of sound localization.
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- Type
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- Language
- en
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- https://doi.org/10.5281/zenodo.6797854
- OA Status
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https://openalex.org/W4283811880Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.5281/zenodo.6797854Digital Object Identifier
- Title
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Replicating Human Sound Localization with a Multi-Layer PerceptronWork title
- Type
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paratextOpenAlex work type
- Language
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enPrimary language
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2022Year of publication
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2022-06-07Full publication date if available
- Authors
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Eric Michael Sumner, Rúnar Unnþórsson, Morris RiedelList of authors in order
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https://doi.org/10.5281/zenodo.6797854Publisher landing page
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
- OA URL
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https://doi.org/10.5281/zenodo.6797854Direct OA link when available
- Concepts
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Computer science, Perceptron, Layer (electronics), Sound (geography), Artificial intelligence, Speech recognition, Artificial neural network, Acoustics, Physics, Materials science, Composite materialTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
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2025: 1, 2022: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.directions. | 100 |
| abstract_inverted_index.individuals | 70, 145, 175 |
| abstract_inverted_index.multi-layer | 126 |
| abstract_inverted_index.perceptrons | 127 |
| abstract_inverted_index.Head-related | 60 |
| abstract_inverted_index.capabilities | 4, 215 |
| abstract_inverted_index.identifying, | 196 |
| abstract_inverted_index.individuals. | 141 |
| abstract_inverted_index.listener’s | 51, 95 |
| abstract_inverted_index.localization | 41 |
| abstract_inverted_index.representing | 88 |
| abstract_inverted_index.successfully | 223 |
| abstract_inverted_index.distribution. | 189 |
| abstract_inverted_index.localization. | 26, 230 |
| abstract_inverted_index.localization; | 118 |
| abstract_inverted_index.hyperparameters | 150, 163 |
| abstract_inverted_index.previously-reported | 213 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.52002805 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |