Re-annotation of cough events in the AMI corpus
| Autor: | Paul Leamy, David Dorran, Ted Burke, Damon Berry |
|---|---|
| Přispěvatelé: | TU Dublin, Fisoraigh Scholarship |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Signal Processing (eess.SP)
Biomedical Computer science 02 engineering and technology acoustic event detection manual annotation computer.software_genre AMI Corpus 03 medical and health sciences Annotation 0302 clinical medicine cough database Audio and Speech Processing (eess.AS) Acoustic event detection FOS: Electrical engineering electronic engineering information engineering 0202 electrical engineering electronic engineering information engineering Electrical Engineering and Systems Science - Signal Processing Audio signal business.industry Event (computing) Physical health respiratory tract diseases cough event detection 030228 respiratory system Manual annotation Signal Processing 020201 artificial intelligence & image processing Artificial intelligence business computer Natural language processing Electrical Engineering and Systems Science - Audio and Speech Processing |
| Zdroj: | Conference papers |
| Popis: | 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. Data presented in this work is available to download using the link included in the references |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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