  Audio-based classification techniques on body sounds have long been studied
to aid in the diagnosis of respiratory diseases. While most research is
centered on the use of cough as the main biomarker, other body sounds also have
the potential to detect respiratory diseases. Recent studies on COVID-19 have
shown that breath and speech sounds, in addition to cough, correlate with the
disease. Our study proposes Fused Audio Instance and Representation (FAIR) as a
method for respiratory disease detection. FAIR relies on constructing a joint
feature vector from various body sounds represented in waveform and spectrogram
form. We conducted experiments on the use case of COVID-19 detection by
combining waveform and spectrogram representation of body sounds. Our findings
show 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 the
Receiver Operating Characteristic Curve (AUC) score of 0.8658, a sensitivity of
0.8057, and a specificity of 0.7958. Compared to models trained solely on
spectrograms or waveforms, the use of both representations results in an
improved AUC score, demonstrating that combining spectrogram and waveform
representation helps to enrich the extracted features and outperforms the
models that use only one representation.
