# Estimating Respiratory Rate From Breath Audio Obtained Through Wearable Microphones

arXiv: https://arxiv.org/abs/2107.14028 · HTML: https://mlsanity.com/paper/2107.14028/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 26 references that are in the map.
- First posted on arXiv 28 Jul 2021.

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

- [Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics](https://mlsanity.com/paper/1705.07115/) (2017)
- [End-to-End Multi-Task Learning with Attention](https://mlsanity.com/paper/1803.10704/) (2018)
- [Detecting Emotion Primitives from Speech and their use in discerning Categorical Emotions](https://mlsanity.com/paper/2002.01323/) (2020)
- [End-to-end losses based on speaker basis vectors and all-speaker hard negative mining for speaker verification](https://mlsanity.com/paper/1902.02455/) (2019)
- [Balance-Oriented Focal Loss with Linear Scheduling for Anchor Free Object Detection](https://mlsanity.com/paper/2012.13763/) (2020)

## Built on by (most cited first, 6 of 6)

- [PhysioMTL: Personalizing Physiological Patterns using Optimal Transport Multi-Task Regression](https://mlsanity.com/paper/2203.12595/) (2022)
- [Model-driven Heart Rate Estimation and Heart Murmur Detection based on Phonocardiogram](https://mlsanity.com/paper/2407.18424/) (2024)
- [SpiroMask: Measuring Lung Function Using Consumer-Grade Masks](https://mlsanity.com/paper/2201.09280/) (2022)
- [Wireless Earphone-based Real-Time Monitoring of Breathing Exercises: A Deep Learning Approach](https://mlsanity.com/paper/2404.10310/) (2024)
- [Mindfulness Meditation and Respiration: Accelerometer-Based Respiration Rate and Mindfulness Progress Estimation to Enhance App Engagement and Mindfulness Skills](https://mlsanity.com/paper/2507.17688/) (2025)
- [Detecting and measuring respiratory events in horses during exercise with a microphone: deep learning vs. standard signal processing](https://mlsanity.com/paper/2508.02349/) (2025)

## Abstract (arXiv)

Respiratory rate (RR) is a clinical metric used to assess overall health and physical fitness. An individual's RR can change from their baseline due to chronic illness symptoms (e.g., asthma, congestive heart failure), acute illness (e.g., breathlessness due to infection), and over the course of the day due to physical exhaustion during heightened exertion. Remote estimation of RR can offer a cost-effective method to track disease progression and cardio-respiratory fitness over time. This work investigates a model-driven approach to estimate RR from short audio segments obtained after physical exertion in healthy adults. Data was collected from 21 individuals using microphone-enabled, near-field headphones before, during, and after strenuous exercise. RR was manually annotated by counting perceived inhalations and exhalations. A multi-task Long-Short Term Memory (LSTM) network with convolutional layers was implemented to process mel-filterbank energies, estimate RR in varying background noise conditions, and predict heavy breathing, indicated by an RR of more than 25 breaths per minute. The multi-task model performs both classification and regression tasks and leverages a mixture of loss functions. It was observed that RR can be estimated with a concordance correlation coefficient (CCC) of 0.76 and a mean squared error (MSE) of 0.2, demonstrating that audio can be a viable signal for approximating RR.