# Re-annotation of cough events in the AMI corpus

## TL;DR

This paper re-annotates cough events in the AMI corpus to create a reliable, publicly available database for developing and evaluating machine learning algorithms for audio cough detection, addressing privacy concerns.

## Contribution

It introduces a new re-annotation methodology and provides a publicly accessible, annotated cough sound database based on the AMI corpus.

## Key findings

- Re-annotated 1369 cough events in the AMI corpus.
- Developed a MATLAB GUI for efficient annotation.
- Made the cough annotations and tool publicly available.

## Abstract

Cough sounds act as an important indicator of an individual's physical health, often used by medical professionals in diagnosing a patient's ailments. In recent years progress has been made in the area of automatically detecting cough events and, in certain cases, automatically identifying the ailment associated with a particular cough sound. Ethical and sensitivity issues associated with audio recordings of coughs makes it more difficult for this data to be made publicly available. However, without the public availability of a reliable database of cough sounds, developments in the area of audio event detection are likely to be hampered. The purpose of this paper is to spread awareness of a database containing a large amount of naturally occurring cough sounds that can be used for the implementation, evaluation, and comparison of new machine learning algorithms that allow for audio event detection associated with cough sounds. Using a purpose built GUI designed in MATLAB, the re-annotation procedure followed a reusable methodology that allowed for quick and efficient importing and marking of audio signals, resulting in a re-annotated version of the Augmented Multi-party Interaction (AMI) corpus' cough location annotations, with 1369 individual cough events. All cough annotations and the re-annotation tool are made available for download and public use.