## 2 Materials and Methods
### Automatic BS Extraction on the Basis of Acoustic Features  
The acoustic feature presented to the ANN is either MFCC or PNCC. MFCC is widely used in fields such as speech recognition and analysis of biological sounds such as lung or heart sounds [9; 16; 17; 18]. MFCC is calculated by performing a discrete cosine transformation on the output from triangular filter banks evenly spaced along a logarithmic axis; this is referred to as a mel scale, and it approximates the human auditory frequency response. PNCC is a feature value developed to improve the robustness of voice recognition systems in noisy environments [11; 12; 13; 14]. Because BS captured using noncontact microphones are generally low in volume and have degraded SNR, PNCC can be expected to be effective; it improves the process of calculating MFCC to make it more similar to certain physiological aspects of humans. Moreover, PNCC differs from MFCC primarily in the following three ways: First, instead of the triangular filter banks used in MFCC, PNCC uses gamma-tone filter banks based on an equivalent rectangular bandwidth to imitate the workings of the cochlea. Second, it uses bias subtraction based on the ratio of the arithmetic mean to the geometric mean (AM-to-GM ratio) for the sound that undergoes intermediate processing, which is not done in the MFCC calculation process. Third, it replaces the logarithmic nonlinearity (used in MFCC) with power nonlinearity. Owing to these differences, PNCC is expected to provide sound processing with excellent resistance to noise. For BS extraction in this work, a SE is divided into frames with a frame size of 200 samples and a shift size of 100 samples. Considering the number of dimensions often used in the field of voice recognition, we use 13-dimension MFCC and PNCC obtained from 24-channel filter banks, averaged over all the frames in each episode.  
On the basis of these acoustic features, an artificial neural network (ANN) is used as a classifier to categorize segments detected with the STE method into BS segments and non-BS segments. The ANN is structured as a hierarchical neural network made up of three layers: namely, the input, intermediate, and output layers. The number of units in the input, intermediate, and output layers are, respectively, 13, 25 and 1. The output function of the intermediate layer units is a hyperbolic tangent function, and the transfer function of the output layer units is a linear function. As a target signal, the value of 1 is assigned to analysis sections in which sound is present if the sound is BS, whereas 0 is assigned if it is non-BS. The ANN learns from this categorization using an error back-propagation algorithm based on the Levenberg-Marquardt method [19; 20]. To calculate sensitivity and specificity based on the post-training ANN output, a receiver operating characteristic (ROC) curve can be drawn. Through the analysis of the ROC curve, an optimum threshold (\(T_{\rm h}\)) is estimated for use when classifying testing data sets. The optimum threshold used at this point is the threshold that is the shortest Euclidean distance from the positions at which sensitivity = 1 and specificity = 1 on the ROC curve [21]. Using this threshold for the ANN test output \(\hat{b}\), it is possible to calculate the classification accuracy using sensitivity (Sen), specificity (Spe), positive predictive value (PPV), negative predictive value (NPV), and accuracy (Acc).  
As shown in Figure 1, automatic BS extraction performance in this ANN-based method is evaluated by dividing the BS and non-BS segments obtained from the 20-person sound database at a ratio of 3:1, and using them respectively as training and testing data. This study calculated the average classification accuracy by performing multiple trials of ANN training and testing, in which (1) initial values of combined load were randomly assigned or (2) test data was randomly assigned.