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Enhancing SELD Performance: The Role of Data Augmentation Techniques in Spatial Sound Analysis.
Christian Santamaria1, Felipe Grijalva1, Karen Rosero2
1Colegio de Ciencias e Ingenierías "El Politécnico", Universidad San Francisco de Quito USFQ, Diego de Robles S/N, Quito 170157, Ecuador.
Sensors (Basel, Switzerland)
|June 12, 2026
Summary
Data augmentation significantly improves Sound Event Localization and Detection (SELD) performance by reducing errors. Augmentation choices are less critical than the decision to augment for better spatial audio models.
Area of Science:
- Artificial Intelligence
- Machine Learning
- Audio Signal Processing
Background:
- Sound Event Localization and Detection (SELD) combines Sound Event Detection (SED) and Direction-of-Arrival Estimation (DOAE).
- Deep learning models show promise for SELD but require extensive, high-quality spatial audio data for generalization.
- Limited availability of spatial audio data is a key challenge for training robust SELD models.
Purpose of the Study:
- To investigate the effectiveness of data augmentation techniques for improving Sound Event Localization and Detection (SELD) model performance.
- To evaluate the impact of specific augmentation methods (Frequency Shift, Random Cutout, Channel Swapping) on SELD accuracy.
- To determine if the choice of augmentation technique or the act of augmentation itself is more critical for SELD enhancement.
Main Methods:
- Implemented and evaluated three data augmentation techniques: Frequency Shift (FS), Random Cutout (RC), and Channel Swapping (CS).
- Conducted experiments to measure the impact of these techniques, individually and in combination, on SELD performance metrics.
- Compared augmentation results against a baseline SELD model trained without data augmentation.
Main Results:
- All tested data augmentation combinations, except Frequency Shift alone, significantly improved SELD performance.
- Data augmentation reduced the SELD error by approximately 8% compared to the baseline model.
- The differences in performance among effective augmentation combinations were not statistically significant.
Conclusions:
- Data augmentation plays a critical role in enhancing the performance of Sound Event Localization and Detection (SELD) systems.
- The decision to apply data augmentation is more impactful than the specific combination of techniques used.
- Future research should explore additional augmentation methods and their application with diverse SELD model architectures.
