# Small-Footprint Keyword Spotting on Raw Audio Data with Sinc-Convolutions

arXiv: https://arxiv.org/abs/1911.02086 · HTML: https://mlsanity.com/paper/1911.02086/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Nov 2019.

## Built on by (most cited first, 20 of 20)

- [Deep Spoken Keyword Spotting: An Overview](https://mlsanity.com/paper/2111.10592/) (2021)
- [How to Design a Three-Stage Architecture for Audio-Visual Active Speaker Detection in the Wild](https://mlsanity.com/paper/2106.03932/) (2021)
- [A Streaming End-to-End Framework For Spoken Language Understanding](https://mlsanity.com/paper/2105.10042/) (2021)
- [Personalized Keyword Spotting through Multi-task Learning](https://mlsanity.com/paper/2206.13708/) (2022)
- [Ultra-Low Power Keyword Spotting at the Edge](https://mlsanity.com/paper/2111.04988/) (2021)
- [Boosting keyword spotting through on-device learnable user speech characteristics](https://mlsanity.com/paper/2403.07802/) (2024)
- [Filterbank Learning for Noise-Robust Small-Footprint Keyword Spotting](https://mlsanity.com/paper/2211.10565/) (2022)
- [Speech Understanding on Tiny Devices with A Learning Cache](https://mlsanity.com/paper/2311.18188/) (2023)
- [End-to-End Efficiency in Keyword Spotting: A System-Level Approach for Embedded Microcontrollers](https://mlsanity.com/paper/2509.07051/) (2025)
- [End-to-end Keyword Spotting using Neural Architecture Search and Quantization](https://mlsanity.com/paper/2104.06666/) (2021)
- [Encoder-Decoder Neural Architecture Optimization for Keyword Spotting](https://mlsanity.com/paper/2106.02738/) (2021)
- [Sub 8-Bit Quantization of Streaming Keyword Spotting Models for Embedded Chipsets](https://mlsanity.com/paper/2207.06920/) (2022)
- [To Wake-up or Not to Wake-up: Reducing Keyword False Alarm by Successive Refinement](https://mlsanity.com/paper/2304.03416/) (2023)
- [A Fast Network Exploration Strategy to Profile Low Energy Consumption for Keyword Spotting](https://mlsanity.com/paper/2202.02361/) (2022)
- [Behavior of Keyword Spotting Networks Under Noisy Conditions](https://mlsanity.com/paper/2109.07930/) (2021)
- [A Study of Designing Compact Audio-Visual Wake Word Spotting System Based on Iterative Fine-Tuning in Neural Network Pruning](https://mlsanity.com/paper/2202.08509/) (2022)
- [A 14uJ/Decision Keyword Spotting Accelerator with In-SRAM-Computing and On Chip Learning for Customization](https://mlsanity.com/paper/2205.04665/) (2022)
- [Keyword Spotting with Hyper-Matched Filters for Small Footprint Devices](https://mlsanity.com/paper/2508.04857/) (2025)
- [Keyword Mamba: Spoken Keyword Spotting with State Space Models](https://mlsanity.com/paper/2508.07363/) (2025)
- [SincDPNet: Interpretable Raw-Waveform Bathroom Activity Recognition for Assistive Living](https://mlsanity.com/paper/2609.34907/) (2026)

## Abstract (arXiv)

Keyword Spotting (KWS) enables speech-based user interaction on smart devices. Always-on and battery-powered application scenarios for smart devices put constraints on hardware resources and power consumption, while also demanding high accuracy as well as real-time capability. Previous architectures first extracted acoustic features and then applied a neural network to classify keyword probabilities, optimizing towards memory footprint and execution time. Compared to previous publications, we took additional steps to reduce power and memory consumption without reducing classification accuracy. Power-consuming audio preprocessing and data transfer steps are eliminated by directly classifying from raw audio. For this, our end-to-end architecture extracts spectral features using parametrized Sinc-convolutions. Its memory footprint is further reduced by grouping depthwise separable convolutions. Our network achieves the competitive accuracy of 96.4% on Google's Speech Commands test set with only 62k parameters.