arXiv:2606.23761v1 Announce Type: cross Abstract: Spiking neural network (SNN)-based neuromorphic speech enhancement has emerged as a promising paradigm due to its energy efficiency, yet it still underperforms classical artificial neural network (ANN)-based approaches owing to binary activations and the lack of well-designed network architectures. To overcome this limitation, we propose a novel dual-branch spiking neural network architecture equipped with a gated spiking unit (GSU), termed GSU-DBNet. Specifically, GSU-DBNet simultaneously models the speech magnitude spectrum and complex spectr
The paper addresses a current limitation in neuromorphic computing, specifically the performance gap between SNNs and ANNs for tasks like speech enhancement, driven by the ongoing push for more energy-efficient AI.
This research contributes to making energy-efficient spiking neural networks more competitive with traditional deep learning, which is crucial for edge AI, mobile devices, and sustainable AI infrastructure.
The development of more effective SNN architectures and training methodologies could accelerate the adoption of neuromorphic hardware for a wider range of AI applications, especially where power consumption is a key constraint.
- · Neuromorphic hardware manufacturers
- · Edge AI device makers
- · Speech technology companies
- · AI researchers in SNNs
- · Traditional high-power computing for certain edge AI tasks
- · Legacy speech enhancement algorithms
Improved energy efficiency in speech processing at the edge.
Faster development and deployment of neuromorphic chips for consumer electronics and industrial applications.
Potential for a new wave of AI devices with unprecedented battery life and real-time processing capabilities.
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Read at arXiv cs.AI