ArticleFrontiers in neuroscience2018
A Spiking Neural Network Framework for Robust Sound Classification.
Article in Frontiers in neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Seed-based resting-state connectivity as a neurosignature in fibromyalgia and depression: a narrative systematic review.Frontiers in human neuroscience · 2025Pooled it
- A frugal Spiking Neural Network for unsupervised multivariate temporal pattern classification and multichannel spike sorting.Nature communications · 2025Article
- Spiking neural networks for EEG signal analysis using wavelet transform.Frontiers in neuroscience · 2025Article
- Snn and sound: a comprehensive review of spiking neural networks in sound.Biomedical engineering letters · 2024Review
- Audio Signal-Stimulated Multilayered HfONanomaterials (Basel, Switzerland) · 2024Article
- Energy-Efficient PPG-Based Respiratory Rate Estimation Using Spiking Neural Networks.Sensors (Basel, Switzerland) · 2024Article
- Advancing brain-inspired computing with hybrid neural networks.National science review · 2024Review
- Adaptive structure evolution and biologically plausible synaptic plasticity for recurrent spiking neural networks.Scientific reports · 2023Article
- Constrain Bias Addition to Train Low-Latency Spiking Neural Networks.Brain sciences · 2023Article
- Learnable axonal delay in spiking neural networks improves spoken word recognition.Frontiers in neuroscience · 2023Article
- Direct learning-based deep spiking neural networks: a review.Frontiers in neuroscience · 2023Review
- Implementing in-situ self-organizing maps with memristor crossbar arrays for data mining and optimization.Nature communications · 2022Article
- Voltage slope guided learning in spiking neural networks.Frontiers in neuroscience · 2022Article
- ALSA: Associative Learning Based Supervised Learning Algorithm for SNN.Frontiers in neuroscience · 2022Article
- A surrogate gradient spiking baseline for speech command recognition.Frontiers in neuroscience · 2022Article
- Using a Low-Power Spiking Continuous Time Neuron (SCTN) for Sound Signal Processing.Sensors (Basel, Switzerland) · 2021Article
- Spatio-Temporal Sequential Memory Model With Mini-Column Neural Network.Frontiers in neuroscience · 2021Article
- Neuronal-Plasticity and Reward-Propagation Improved Recurrent Spiking Neural Networks.Frontiers in neuroscience · 2021Article
- Deep Spiking Neural Networks for Large Vocabulary Automatic Speech Recognition.Frontiers in neuroscience · 2020Article
- Odor Recognition with a Spiking Neural Network for Bioelectronic Nose.Sensors (Basel, Switzerland) · 2019Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Environmental sounds form part of our daily life. With the advancement of deep learning models and the abundance of training data, the performance of automatic sound classification (ASC) systems has improved significantly in recent years. However, the high computational cost, hence high power consumption, remains a major hurdle for large-scale implementation of ASC systems on mobile and wearable devices. Motivated by the observations that humans are highly effective and consume little power whilst analyzing complex audio scenes, we propose a biologically plausible ASC framework, namely SOM-SNN. This framework uses the unsupervised self-organizing map (SOM) for representing frequency contents embedded within the acoustic signals, followed by an event-based spiking neural network (SNN) for spatiotemporal spiking pattern classification. We report experimental results on the RWCP environmental sound and TIDIGITS spoken digits datasets, which demonstrate competitive classification accuracies over other deep learning and SNN-based models. The SOM-SNN framework is also shown to be highly robust to corrupting noise after multi-condition training, whereby the model is trained with noise-corrupted sound samples. Moreover, we discover the early decision making capability of the proposed framework: an accurate classification can be made with an only partial presentation of the input.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.