## 2 Materials and Methods
### Methods  
In Figure 1, we depict the workflow related to the method that we propose. The audio sessions related to the breath of the patient are recorded exploiting, for instance, digital stethoscopes [9]. As a matter of fact, nowadays, electronic stethoscopes convert the acoustic sound waves obtained through the chest piece into electrical signals that are successively amplified for better listening [10]. Once we obtained the audio sample related to the patient's breath, we computed a set of numeric values, i.e., a feature vector directly computed on the breath sound sample. In detail, the following features were computed:  
* Chromagram (_CR_): this feature is related to a chromagram representation automatically gathered from a waveform (\(F1\) feature);
* Root Mean Square (_RMS_): this feature (i.e., _RMS_) is related the value of the mean square as the root that is obtained for each audio frame that is gathered from the sound sample under analysis (\(F2\) feature);
* Spectral Centroid (_SC_): this feature is symptomatic of the \"centre of mass\" for a sound sample and is obtained as the mean related to the frequencies of the audio (\(F3\) feature);
* Bandwidth: it is related the bandwidth of the spectrum (\(F4\) feature);
* Spectral Roll-Off (_SR_): it is expressed as the frequency related to a certain percentage of the total spectral of the energy (\(F5\) feature);
* Tonnetz (\(T\)): it is computed from the the tonal centroid (\(F6\) feature). * Mel-Frequency Cepstral Coefficient: this feature (i.e., _MEL_), whose acronym is related to a feature vector (ranging from 10 to 20 different numerical features 10-20), is devoted to representing the shape of a spectral envelope (\(F7\) feature);
* Zero Crossing Rate (_ZCR_): this value is related to the rate of an audio time series (\(F8\) feature);
* Poly (\(P\)): it is computed as the fitting coefficient related to an \(n\)th-order polynomial (\(F9\) feature). Mathematical details about the feature vector that we considered can be found in [11; 12; 13; 14]. We consider this feature set due to its demonstrated effectiveness in performing other tasks involving supervised machine learning--for instance, the classification and segmentation of audio files into generic classes as speech [15], music [16], and silence [17; 18; 19]. The idea is to obtain a numeric vector for each audio sample; as a matter of fact, machine learning typically works with numerical values. Once the feature vector is obtained, these values are converted into a CSV file (i.e., _Data preprocessing_ in Figure 1). In particular, the authors developed a script by exploiting the Java programming language that aimed to automatically extract the numeric features from each audio sample and to generate a CSV file, where, in each row, there are the numerical features from a single audio sample. With the script, the authors verified whether, for each audio sample considered in the dataset, all the numeric features had been correctly extracted, with the aim of avoiding inconsistencies. We consider raw features in the feature vector. We are aware that feature normalization is beneficial in many cases; as a matter of fact, it improves the numerical stability of the model and often reduces the training time. However, it can harm the performance of distance-based clustering algorithms by assuming the equal importance of features. If there are inherent importance differences between features, typically, we do not exploit the  
Figure 1: The workflow of the proposed approach for lung disease detection. normalisation of the features. For instance, neural networks can counteract standardization in the same way as regressions. Therefore, in theory, data standardization should not affect the performance of a neural network. These are the reasons that we do not consider feature normalisation. This CSV file is sent to the _lung disease detection_ module.