Fundamental Survey on Neuromorphic Based Audio Classification
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Abstract (arXiv)
Audio classification is paramount in a variety of applications including surveillance, healthcare monitoring, and environmental analysis. Traditional methods frequently depend on intricate signal processing algorithms and manually crafted features, which may fall short in fully capturing the complexities of audio patterns. Neuromorphic computing, inspired by the architecture and functioning of the human brain, presents a promising alternative for audio classification tasks. This survey provides an exhaustive examination of the current state-of-the-art in neuromorphic-based audio classification. It delves into the crucial components of neuromorphic systems, such as Spiking Neural Networks (SNNs), memristors, and neuromorphic hardware platforms, highlighting their advantages in audio classification. Furthermore, the survey explores various methodologies and strategies employed in neuromorphic audio classification, including event-based processing, spike-based learning, and bio-inspired feature extraction. It examines how these approaches address the limitations of traditional audio classification methods, particularly in terms of energy efficiency, real-time processing, and robustness to environmental noise. Additionally, the paper conducts a comparative analysis of different neuromorphic audio classification models and benchmarks, evaluating their performance metrics, computational efficiency, and scalability. By providing a comprehensive guide for researchers, engineers and practitioners, this survey aims to stimulate further innovation and advancements in the evolving field of neuromorphic audio classification.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 20 of its 40 references that are in the map. First posted on arXiv 20 Feb 2025.
Builds on
20 of 40 references.
References · 20
- 2018Speech Commands: A Dataset for Limited-Vocabulary Speech Recognitioncited by 752
- 2020VGGSound: A Large-scale Audio-Visual Datasetcited by 648
- 2020FSD50K: An Open Dataset of Human-Labeled Sound Eventscited by 342
- 2019The Heidelberg spiking datasets for the systematic evaluation of spiking neural networkscited by 137
- 2021Accurate and efficient time-domain classification with adaptive spiking recurrent neural networkscited by 115
- 2021Temporal-wise Attention Spiking Neural Networks for Event Streams Classificationcited by 106
- 2023Learning Delays in Spiking Neural Networks using Dilated Convolutions with Learnable Spacingscited by 50
- 2020Effective and Efficient Computation with Multiple-timescale Spiking Recurrent Neural Networkscited by 43
- 2022STSC-SNN: Spatio-Temporal Synaptic Connection with Temporal Convolution and Attention for Spiking Neural Networkscited by 20
- 2023DenRAM: Neuromorphic Dendritic Architecture with RRAM for Efficient Temporal Processing with Delayscited by 17
- 2022Fluctuation-driven initialization for spiking neural network trainingcited by 16
- 2022Loss shaping enhances exact gradient learning with Eventprop in spiking neural networkscited by 14
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- 2019Spiking neural networks trained with backpropagation for low power neuromorphic implementation of voice activity detectioncited by 10
- 2021Keys to Accurate Feature Extraction Using Residual Spiking Neural Networkscited by 10
- 2022Simple and complex spiking neurons: perspectives and analysis in a simple STDP scenariocited by 6
- 2024sVAD: A Robust, Low-Power, and Light-Weight Voice Activity Detection with Spiking Neural Networkscited by 4
- 2023HPCNeuroNet: Advancing Neuromorphic Audio Signal Processing with Transformer-Enhanced Spiking Neural Networkscited by 2
- 2024Evolutionary Spiking Neural Networks: A Surveycited by 2
- 2024Point Neuron Learning: A New Physics-Informed Neural Network Architecturecited by 2
- 2019Acoustic Scene Analysis using Analog Spiking Neural Networkcited by 1
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Citing papers per year: 2026: 1.
1 paper.