# LearnAFE: Circuit-Algorithm Co-design Framework for Learnable Audio Analog Front-End

arXiv: https://arxiv.org/abs/2507.00755 · HTML: https://mlsanity.com/paper/2507.00755/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 37 references that are in the map.
- First posted on arXiv 1 Jul 2025.

## Builds on (most cited first, 10 of 10)

- [Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition](https://mlsanity.com/paper/1804.03209/) (2018)
- [Hello Edge: Keyword Spotting on Microcontrollers](https://mlsanity.com/paper/1711.07128/) (2017)
- [Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification](https://mlsanity.com/paper/1608.04363/) (2016)
- [LEAF: A Learnable Frontend for Audio Classification](https://mlsanity.com/paper/2101.08596/) (2021)
- [Learning Filterbanks from Raw Speech for Phone Recognition](https://mlsanity.com/paper/1711.01161/) (2017)
- [FastAudio: A Learnable Audio Front-End for Spoof Speech Detection](https://mlsanity.com/paper/2109.02774/) (2021)
- [Sub-mW Keyword Spotting on an MCU: Analog Binary Feature Extraction and Binary Neural Networks](https://mlsanity.com/paper/2201.03386/) (2022)
- [A 23 μW Keyword Spotting IC with Ring-Oscillator-Based Time-Domain Feature Extraction](https://mlsanity.com/paper/2208.00693/) (2022)
- [Filterbank Learning for Noise-Robust Small-Footprint Keyword Spotting](https://mlsanity.com/paper/2211.10565/) (2022)
- [A systematic study comparing hyperparameter optimization engines on tabular data](https://mlsanity.com/paper/2311.15854/) (2023)

## Abstract (arXiv)

This paper presents a circuit-algorithm co-design framework for learnable analog front-end (AFE) in audio signal classification. Designing AFE and backend classifiers separately is a common practice but non-ideal, as shown in this paper. Instead, this paper proposes a joint optimization of the backend classifier with the AFE's transfer function to achieve system-level optimum. More specifically, the transfer function parameters of an analog bandpass filter (BPF) bank are tuned in a signal-to-noise ratio (SNR)-aware training loop for the classifier. Using a co-design loss function LBPF, this work shows superior optimization of both the filter bank and the classifier. Implemented in open-source SKY130 130nm CMOS process, the optimized design achieved 90.5%-94.2% accuracy for 10-keyword classification task across a wide range of input signal SNR from 5 dB to 20 dB, with only 22k classifier parameters. Compared to conventional approach, the proposed audio AFE achieves 8.7% and 12.9% reduction in power and capacitor area respectively.