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ABSTRACT ATARI, PANKAJ. Detecting Cough in Audio Recordings with OutofDistribution (GOOD) Data Detection Techniques. (Under the direction of Dr. Edgar Lobaton). Cough is an important defense mechanism
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01
Collect audio samples containing potential cough sounds
02
Preprocess the audio data to remove noise and irrelevant sounds
03
Extract relevant features from the audio data using signal processing techniques
04
Train a machine learning model on labeled cough audio samples to detect cough sounds
05
Test the model on new audio samples to evaluate its performance

Who needs detecting cough in audio?

01
Healthcare professionals monitoring patients remotely
02
Researchers studying trends in respiratory illnesses
03
Pharmaceutical companies conducting clinical trials for cough medications
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Detecting cough in audio refers to the process of using audio analysis techniques to identify and classify cough sounds recorded in various environments, often for health monitoring or diagnostic purposes.
Individuals or entities involved in research, healthcare monitoring, or telemedicine services may be required to file detecting cough in audio if they are collecting or analyzing cough-related data for clinical or regulatory purposes.
To fill out detecting cough in audio, one must collect audio samples of coughs, analyze the data using specific software or algorithms, and document the results along with any relevant patient information or findings in a standardized format.
The purpose of detecting cough in audio is to monitor respiratory conditions, aid in the diagnosis of diseases such as COVID-19 or asthma, and to enhance telehealth services by providing a non-invasive method of patient assessment.
Information that must be reported includes the date and time of audio recording, the patient's demographic details, the context of the recording, cough characteristics, and any preliminary analysis results.
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