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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. 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 AI

Sep 1

Conjoint Audio-to-Spikes Encoding and Processing for Efficient Neuromorphic Speech Recognition

arXiv:2608.30792v1 Announce Type: cross Abstract: Obtaining data from neuromorphic sensors and processing it with Spiking Neural Networks is a promising solution to lower the energy cost of artificia...

By Valentin M. Meunier, Am\'elie Gruel, Pierre Lewden, Adrien F. Vincent, Sylvain Sa\"ighi

multimodal benchmarks

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

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

Conjoint Audio-to-Spikes Encoding and Processing for Efficient Neuromorphic Speech Recognition

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