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Audio-based classification techniques on body sounds have long been studiedto aid in the diagnosis of respiratory diseases. While most research iscentered on the use of cough as the main biomarker, other body sounds also havethe potential to detect respiratory diseases. Recent studies on COVID-19 haveshown that breath and speech sounds, in addition to cough, correlate with thedisease. Our study proposes Fused Audio Instance and Representation (FAIR) as amethod for respiratory disease detection. FAIR relies on constructing a jointfeature vector from various body sounds represented in waveform and spectrogramform. We conducted experiments on the use case of COVID-19 detection bycombining waveform and spectrogram representation of body sounds. Our findingsshow that the use of self-attention to combine extracted features from cough,breath, and speech sounds leads to the best performance with an Area Under theReceiver Operating Characteristic Curve (AUC) score of 0.8658, a sensitivity of0.8057, and a specificity of 0.7958. Compared to models trained solely onspectrograms or waveforms, the use of both representations results in animproved AUC score, demonstrating that combining spectrogram and waveformrepresentation helps to enrich the extracted features and outperforms themodels that use only one representation.

Fused Audio Instance and Representation for Respiratory Disease Detection