arXiv AI June 24, 2026 By Taiyu Meng, Wenbin Jiang, Haoyi Zhang, Yuhan Zhou, Haibing Yin

Neuromorphic Speech Enhancement with Dual-Branch Spiking Neural Networks

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

benchmarks

arXiv Machine Learning

Jun 5

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement

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.

By Cunhang Fan, Enrui Liu, Jing Zhou, Jian Kang, Jie Li, Andong Li, Jian Zhou, Zhao Lv, Xuelong Li

arXiv Machine Learning

Jun 18

Adaptive Speech-to-Spike Encoding for Spiking Neural Networks

arXiv:2606. 19039v1 Announce Type: cross Abstract: The mismatch between continuous acoustic signals and discrete event-driven processing remains a fundamental bottleneck for neuromorphic speech processing.

By Taharim Rahman Anon, Jakaria Islam Emon

benchmarks safety

arXiv AI

Jun 17

A Neuromorphic Trigger for Efficient Audio Event Detection

arXiv:2606. 17775v1 Announce Type: cross Abstract: Efficient processing of continuous audio streams remains a key challenge for real-time and resource-constrained systems.

By Benjamin Hatton, Oliver Rhodes, Luca Peres

arXiv AI

Jun 24

End-to-End Radar and Communication Modulation Recognition with Neuromorphic Computing

arXiv:2606. 24075v1 Announce Type: cross Abstract: Although deep learning-based methods can achieve high accuracy in automatic modulation recognition (AMR) tasks, their high computational cost makes it difficult to strike a balance between accuracy and power consumption, thereby limiting their application on resource-constrained platforms.

By Xiaohu Li, Chongxiao Qu, Caiyong Lin, Chenxiao Dou, Wei Hua

llms benchmarks

arXiv AI

Jun 11

SpikeDecoder: Realizing the GPT Architecture with Spiking Neural Networks

arXiv:2606. 12287v1 Announce Type: cross Abstract: The Transformer architecture is widely regarded as the most powerful tool for natural language processing, but due to a high number of complex operations, it inherently faces the issue of high energy consumption.

By Claas Beger, Florian Walter, Alois Knoll

llms rag computer-vision nlp

arXiv Machine Learning

Jun 16

Neuromorphic Wireless Split Computing with Resonate-and-Fire Neurons

arXiv:2506. 20015v2 Announce Type: replace Abstract: Neuromorphic computing offers an energy-efficient alternative to conventional deep learning accelerators, particularly for real-time processing of time-series data.

By Dengyu Wu, Jiechen Chen, H. Vincent Poor, Bipin Rajendran, Osvaldo Simeone

efficiency

Related stories

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement

Adaptive Speech-to-Spike Encoding for Spiking Neural Networks

A Neuromorphic Trigger for Efficient Audio Event Detection

End-to-End Radar and Communication Modulation Recognition with Neuromorphic Computing

SpikeDecoder: Realizing the GPT Architecture with Spiking Neural Networks

Neuromorphic Wireless Split Computing with Resonate-and-Fire Neurons