This section examines an audio classification algorithm that recognizes coughing and sneezing using an audio sensor with an embedded DL engine. The methodology for audio detection is shown in Figure 13. This figure shows the four main steps of the audio DL process.The recording needs to first be preprocessed for noise before being used for extracting sound features. The most commonly known time-frequency feature is the short-time Fourier transform [67], Mel spectrogram [68], and wavelet spectrogram [69]. The Mel spectrogram was based on a nonlinear frequency scale motivated by human auditory perception and provides a more compact spectral representation of sounds when compared to the STFT [3]. To compute a Mel spectrogram, we first convert the sample audio files into time series. Next, its magnitude spectrogram is computed, and then mapped onto the Mel scale with power 2. The end result would be a Mel spectrogram [70]. The last step in preprocessing would be to convert Mel spectrograms into log Mel spectrograms. Then the image results would be introduced as an input to the deep learning modelling process.