# Wake-Cough: cough spotting and cougher identification for personalised long-term cough monitoring

arXiv: https://arxiv.org/abs/2110.03771 · HTML: https://mlsanity.com/paper/2110.03771/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 28 references that are in the map.
- First posted on arXiv 7 Oct 2021.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition](https://mlsanity.com/paper/1804.03209/) (2018)
- [Generalized End-to-End Loss for Speaker Verification](https://mlsanity.com/paper/1710.10467/) (2017)
- [COVID-19 Cough Classification using Machine Learning and Global Smartphone Recordings](https://mlsanity.com/paper/2012.01926/) (2020)
- [Automatic Cough Classification for Tuberculosis Screening in a Real-World Environment](https://mlsanity.com/paper/2103.13300/) (2021)
- [Towards Data-efficient Modeling for Wake Word Spotting](https://mlsanity.com/paper/2010.06659/) (2020)
- [COVID-19 Detection in Cough, Breath and Speech using Deep Transfer Learning and Bottleneck Features](https://mlsanity.com/paper/2104.02477/) (2021)
- [Deep Neural Network based Cough Detection using Bed-mounted Accelerometer Measurements](https://mlsanity.com/paper/2102.04997/) (2021)
- [Automatic non-invasive Cough Detection based on Accelerometer and Audio Signals](https://mlsanity.com/paper/2109.00103/) (2021)
- [Speaker Recognition with Cough, Laugh and "Wei"](https://mlsanity.com/paper/1706.07860/) (2017)
- [Multilingual Bottleneck Features for Improving ASR Performance of Code-Switched Speech in Under-Resourced Languages](https://mlsanity.com/paper/2011.03118/) (2020)
- [Improved Deep Speaker Feature Learning for Text-Dependent Speaker Recognition](https://mlsanity.com/paper/1506.08349/) (2015)

## Built on by (most cited first, 2 of 2)

- [Automatic Tuberculosis and COVID-19 cough classification using deep learning](https://mlsanity.com/paper/2205.05480/) (2022)
- [Accelerometer-based Bed Occupancy Detection for Automatic, Non-invasive Long-term Cough Monitoring](https://mlsanity.com/paper/2202.03936/) (2022)

## Abstract (arXiv)

We present `wake-cough', an application of wake-word spotting to coughs using a Resnet50 and the identification of coughers using i-vectors, for the purpose of a long-term, personalised cough monitoring system. Coughs, recorded in a quiet (73±5 dB) and noisy (34±17 dB) environment, were used to extract i-vectors, x-vectors and d-vectors, used as features to the classifiers. The system achieves 90.02% accuracy when using an MLP to discriminate between 51 coughers using 2-sec long cough segments in the noisy environment. When discriminating between 5 and 14 coughers using longer (100 sec) segments in the quiet environment, this accuracy improves to 99.78% and 98.39% respectively. Unlike speech, i-vectors outperform x-vectors and d-vectors in identifying coughers. These coughs were added as an extra class to the Google Speech Commands dataset and features were extracted by preserving the end-to-end time-domain information in a trigger phrase. The highest accuracy of 88.58% is achieved in spotting coughs among 35 other trigger phrases using a Resnet50. Thus, wake-cough represents a personalised, non-intrusive cough monitoring system, which is power-efficient as on-device wake-word detection can keep a smartphone-based monitoring device mostly dormant. This makes wake-cough extremely attractive in multi-bed ward environments to monitor patients' long-term recovery from lung ailments such as tuberculosis (TB) and COVID-19.