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June 3, 2026SensorsOpen Access

Enhancing SELD Performance: The Role of Data Augmentation Techniques in Spatial Sound Analysis

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Authors

CSChristian SantamariaFGFelipe GrijalvaKRKaren Rosero

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Overview

Randomized trial tests data augmentation for improving sound event localization and detection performance in machine learning models, suggesting significant benefits overall.

Key Points

  • Investigate the effectiveness of data augmentation techniques in enhancing sound event localization and detection performance.
  • Evaluated Frequency Shift, Random Cutout, and Channel Swapping techniques.
  • Conducted a comprehensive set of experiments to assess the impact on SELD performance.
  • Measured SELD error reduction and analyzed statistical significance of findings.
  • Augmentation techniques improved SELD performance, reducing error by approximately 8%.
  • All combinations except FS alone were effective, with no statistically significant differences among combinations.
  • Emphasized the importance of data augmentation over specific techniques chosen.

Cite This Study

Santamaria et al. (2026) studied this question.

synapsesocial.com/papers/6a1fc76ddee9eb8c0dce85e9https://doi.org/10.3390/s26113466
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