arXiv:2606.31552v1 Announce Type: cross Abstract: Room-acoustic simulations are widely used to augment training data for deep-learning-based speech enhancement. While most pipelines rely on simplified geometrical acoustics, wave-based approaches offer greater physical accuracy. In this work, we examine how simulation fidelity affects multichannel speech enhancement performance. To this end, we train SpatialNet on datasets augmented with different room-acoustic simulation methods and evaluate the resulting models on measured data. We compare lower-fidelity datasets based on geometrical acoustic
The continuous drive for higher fidelity in AI training data necessitates exploration of more sophisticated simulation techniques for robust model performance.
Improving the accuracy of room-acoustic simulations can significantly enhance speech enhancement technologies, impacting various applications from communication to assistive devices.
The understanding of how simulation fidelity directly translates to real-world performance for deep-learning-based speech enhancement models is refined.
- · AI researchers in speech enhancement
- · Developers of audio processing hardware
- · Telecommunication companies
- · Assistive technology providers
- · Companies relying on low-fidelity simulation for critical applications
More accurate deep learning models for speech enhancement due to improved training data.
Better performance of voice assistants, conferencing tools, and hearing aids in complex acoustic environments.
Accelerated development of AI agents capable of understanding nuances in human speech across diverse real-world conditions.
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Read at arXiv cs.LG