arXiv:2606.05911v1 Announce Type: cross Abstract: Although artificial neural network (ANN) based speech enhancement (SE) methods demonstrate excellent performance, the high computational complexity and high energy consumption hinder their deployment in practical front-end processing tasks.} Currently, the spiking neural networks (SNNs) have shown potential in reducing power consumption. However, the discrete binary activation and complex spatio-temporal dynamics of SNNs often result in information loss. The current challenge therefore focuses on how to maintain performance and reduce computati
The increasing deployment of AI in edge devices and the pervasive use of speech enhancement necessitate more efficient, lower-power computational models for real-world application.
Reducing computational complexity and energy consumption in AI models is critical for broadening their applicability, especially in portable and resource-constrained environments.
This research contributes to the ongoing effort to make advanced AI processing, like speech enhancement, more accessible and sustainable for wider deployment beyond high-performance computing scenarios.
- · Edge AI device manufacturers
- · Consumer electronics industry
- · Hearing aid developers
- · Speech technology companies
- · High-power ANN developers for edge cases
- · Traditional high-complexity SE methods
More efficient speech enhancement could enable longer battery life and better performance in mobile devices and IoT.
The development could accelerate the integration of advanced audio processing into a wider array of low-cost, pervasive devices.
This efficiency gain might contribute to reducing the overall energy footprint of global AI systems, addressing the energy bottleneck indirectly.
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Read at arXiv cs.LG