On The Relevance Of The Differences Between HRTF Measurement Setups For Machine Learning

• URL: http://arxiv.org/abs/2212.04283v1
• Date: Thu, 8 Dec 2022 14:19:46 GMT
• Title: On The Relevance Of The Differences Between HRTF Measurement Setups For Machine Learning
• Authors: Johan Pauwels and Lorenzo Picinali
• Abstract summary: spatial audio is enjoying a surge in popularity. Machine learning techniques that have been proven successful in other domains are increasingly used to process head-related transfer function measurements. It becomes attractive to combine multiple datasets, although they are measured under different conditions.
• Score: 0.24366811507669117
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As spatial audio is enjoying a surge in popularity, data-driven machine learning techniques that have been proven successful in other domains are increasingly used to process head-related transfer function measurements. However, these techniques require much data, whereas the existing datasets are ranging from tens to the low hundreds of datapoints. It therefore becomes attractive to combine multiple of these datasets, although they are measured under different conditions. In this paper, we first establish the common ground between a number of datasets, then we investigate potential pitfalls of mixing datasets. We perform a simple experiment to test the relevance of the remaining differences between datasets when applying machine learning techniques. Finally, we pinpoint the most relevant differences.

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