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- W4310561090 abstract "Learning-based methods have become ubiquitous in speaker localization. Existing systems rely on simulated training sets for the lack of sufficiently large, diverse and annotated real datasets. Most room acoustics simulators used for this purpose rely on the image source method (ISM) because of its computational efficiency. This paper argues that carefully extending the ISM to incorporate more realistic surface, source and microphone responses into training sets can significantly boost the real-world performance of speaker localization systems. It is shown that increasing the training-set realism of a state-of-the-art direction-of-arrival estimator yields consistent improvements across three different real test sets featuring human speakers in a variety of rooms and various microphone arrays. An ablation study further reveals that every added layer of realism contributes positively to these improvements." @default.
- W4310561090 created "2022-12-12" @default.
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- W4310561090 date "2022-11-30" @default.
- W4310561090 modified "2023-09-25" @default.
- W4310561090 title "How to (virtually) train your speaker localizer" @default.
- W4310561090 doi "https://doi.org/10.48550/arxiv.2211.16958" @default.
- W4310561090 hasPublicationYear "2022" @default.
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