arXiv (OAI Expanded)
open access
Conjoint Audio-to-Spikes Encoding and Processing for Efficient Neuromorphic Speech Recognition
Obtaining data from neuromorphic sensors and processing it with Spiking Neural Networks is a promising solution to lower the energy cost of artificial intelligence. The current rarity of natively neuromorphic datasets promotes the development of software tools to translate input sensory data into sp
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Conjoint Audio-to-Spikes Encoding and Processing for Efficient Neuromorphic Speech Recognition#818839
- Exploring Neuromorphic Computing Based on Spiking Neural Networks: Algorithms to Hardware#19786
- Zephyr: An Efficient Audio Denoising System Using Spiking Neural Networks Enabled With A Sparsity-Aware Flexible FPGA PE Array#1142023
- Optimizing Deeper Spiking Neural Networks for Dynamic Vision Sensing#19788
- Latency and accuracy tradeoffs in Spiking Neural Networks#1129854
Record · ID 808847
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive