Speech2Spikes: Efficient Audio Encoding Pipeline for Real-time Neuromorphic Systems
Author:
Affiliation:
1. Computer Science, University of California Irvine, United States and Accenture Labs, United States
2. Intel Labs, United States
3. Accenture Labs, United States
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3584954.3584995
Reference39 articles.
1. Speaker-independent isolated digit recognition using an AER silicon cochlea
2. Feature Representations for Neuromorphic Audio Spike Streams
3. Lukas Biewald. 2020. Weights & Biases - developer tools for ML. Lukas Biewald. 2020. Weights & Biases - developer tools for ML.
4. Peter Blouw Xuan Choo Eric Hunsberger and Chris Eliasmith. 2019. Benchmarking Keyword Spotting Efficiency on Neuromorphic Hardware. arxiv:1812.01739 [cs.LG] Peter Blouw Xuan Choo Eric Hunsberger and Chris Eliasmith. 2019. Benchmarking Keyword Spotting Efficiency on Neuromorphic Hardware. arxiv:1812.01739 [cs.LG]
5. A 240 × 180 130 dB 3 µs Latency Global Shutter Spatiotemporal Vision Sensor
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