ArticleFrontiers in neuroscience2018
Feature Representations for Neuromorphic Audio Spike Streams.
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 19 papers.
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19 citing papers in PubMed, 109 citations in OpenAlex.
- Optimizing event-driven spiking neural network with regularization and cutoff.Frontiers in neuroscience · 2025Article
- Spiking neural networks for physiological and speech signals: a review.Biomedical engineering letters · 2024Review
- Snn and sound: a comprehensive review of spiking neural networks in sound.Biomedical engineering letters · 2024Review
- Composing recurrent spiking neural networks using locally-recurrent motifs and risk-mitigating architectural optimization.Frontiers in neuroscience · 2024Article
- Bioinspired Perception and Navigation of Service Robots in Indoor Environments: A Review.Biomimetics (Basel, Switzerland) · 2023Review
- Event-driven spectrotemporal feature extraction and classification using a silicon cochlea model.Frontiers in neuroscience · 2023Article
- First-spike coding promotes accurate and efficient spiking neural networks for discrete events with rich temporal structures.Frontiers in neuroscience · 2023Article
- Learnable axonal delay in spiking neural networks improves spoken word recognition.Frontiers in neuroscience · 2023Article
- Boost event-driven tactile learning with location spiking neurons.Frontiers in neuroscience · 2023Article
- Spike encoding techniques for IoT time-varying signals benchmarked on a neuromorphic classification task.Frontiers in neuroscience · 2022Article
- Neuromorphic Engineering Needs Closed-Loop Benchmarks.Frontiers in neuroscience · 2022Review
- Extended liquid state machines for speech recognition.Frontiers in neuroscience · 2022Article
- Event-Based Sensing and Signal Processing in the Visual, Auditory, and Olfactory Domain: A Review.Frontiers in neural circuits · 2021Review
- Hand-Gesture Recognition Based on EMG and Event-Based Camera Sensor Fusion: A Benchmark in Neuromorphic Computing.Frontiers in neuroscience · 2020Article
- Deep Spiking Neural Networks for Large Vocabulary Automatic Speech Recognition.Frontiers in neuroscience · 2020Article
- Boosting Throughput and Efficiency of Hardware Spiking Neural Accelerators Using Time Compression Supporting Multiple Spike Codes.Frontiers in neuroscience · 2020Article
- An Efficient and Perceptually Motivated Auditory Neural Encoding and Decoding Algorithm for Spiking Neural Networks.Frontiers in neuroscience · 2019Article
- A Spiking Neural Network Framework for Robust Sound Classification.Frontiers in neuroscience · 2018Article
- A Comparison of Low-Complexity Real-Time Feature Extraction for Neuromorphic Speech Recognition.Frontiers in neuroscience · 2018Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Event-driven neuromorphic spiking sensors such as the silicon retina and the silicon cochlea encode the external sensory stimuli as asynchronous streams of spikes across different channels or pixels. Combining state-of-art deep neural networks with the asynchronous outputs of these sensors has produced encouraging results on some datasets but remains challenging. While the lack of effective spiking networks to process the spike streams is one reason, the other reason is that the pre-processing methods required to convert the spike streams to frame-based features needed for the deep networks still require further investigation. This work investigates the effectiveness of synchronous and asynchronous frame-based features generated using spike count and constant event binning in combination with the use of a recurrent neural network for solving a classification task using N-TIDIGITS18 dataset. This spike-based dataset consists of recordings from the Dynamic Audio Sensor, a spiking silicon cochlea sensor, in response to the TIDIGITS audio dataset. We also propose a new pre-processing method which applies an exponential kernel on the output cochlea spikes so that the interspike timing information is better preserved. The results from the N-TIDIGITS18 dataset show that the exponential features perform better than the spike count features, with over 91% accuracy on the digit classification task. This accuracy corresponds to an improvement of at least 2.5% over the use of spike count features, establishing a new state of the art for this dataset.
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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.