# EdgeCodec: Onboard Lightweight High Fidelity Neural Compressor with Residual Vector Quantization

arXiv: https://arxiv.org/abs/2507.06040 · HTML: https://mlsanity.com/paper/2507.06040/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 20 references that are in the map.
- First posted on arXiv 8 Jul 2025.

## Builds on (most cited first, 5 of 5)

- [SoundStream: An End-to-End Neural Audio Codec](https://mlsanity.com/paper/2107.03312/) (2021)
- [High Fidelity Neural Audio Compression](https://mlsanity.com/paper/2210.13438/) (2022)
- [Time series compression: a survey](https://mlsanity.com/paper/2101.08784/) (2021)
- [Multi-Scale Sub-Band Constant-Q Transform Discriminator for High-Fidelity Vocoder](https://mlsanity.com/paper/2311.14957/) (2023)
- [Aerosense: A Self-Sustainable And Long-Range Bluetooth Wireless Sensor Node for Aerodynamic and Aeroacoustic Monitoring on Wind Turbines](https://mlsanity.com/paper/2205.11902/) (2022)

## Abstract (arXiv)

We present EdgeCodec, an end-to-end neural compressor for barometric data collected from wind turbine blades. EdgeCodec leverages a heavily asymmetric autoencoder architecture, trained with a discriminator and enhanced by a Residual Vector Quantizer to maximize compression efficiency. It achieves compression rates between 2'560:1 and 10'240:1 while maintaining a reconstruction error below 3%, and operates in real time on the GAP9 microcontroller with bitrates ranging from 11.25 to 45 bits per second. Bitrates can be selected on a sample-by-sample basis, enabling on-the-fly adaptation to varying network conditions. In its highest compression mode, EdgeCodec reduces the energy consumption of wireless data transmission by up to 2.9x, significantly extending the operational lifetime of deployed sensor units.