## 1 Introduction  
The decrease in or loss of bowel motility is a problem that seriously affects quality of life (QOL) and daily eating habits of patients; examples of this include functional gastrointestinal disorders (FGID), in which patients experience bloating and pain when bowel motility is impaired due to stress or other factors. Such bowel disorders are diagnosed by evaluating the bowel motility. Bowel motility is currently measured using X-ray imaging or endoscopy techniques; however, these methods require complex testing equipment and place immense mental, physical, and financial burdens on patients, which make these methods unsuitable for repeated monitoring.  
In recent years, the acoustic features obtained from bowel sounds (BS) have been used to evaluate bowel motility. BS are created when transportation of gas and digestive contents through the digestive tract occurs due to peristaltic movement [1]. BS can be easily recorded by applying an electronic stethoscope to the surface of the body. In recent years, a method has been developed for evaluating bowel motility by automatically extracting BS from the audio data recorded using electronic stethoscopes [2; 3; 4; 5; 6; 7]. In quiet conditions, BS can be perceived at a slight distance without the use of an electronic stethoscope. As such, our recent research has demonstrated that even when data is acquired using a noncontact microphone, bowel motility can be evaluated based on BS in a manner the same as that when an electronic stethoscope is used [8]. However, in this study, BS were required to be manually extracted from the audio data that was recorded using noncontact microphones, and a large amount of time was spent on carefully labeling the sounds. The sound pressure of BS recorded using noncontact microphones was lower than that of BS recorded with electronic stethoscopes placed directly on the surface of the body. Furthermore, compared to recordings from electronic stethoscopes, there may have been sounds other than BS mixed in at higher volumes. As such, a BS extraction system that is robust against extraneous noise must be developed to reduce the time- and labor-intensive work of BS labeling.  
To resolve these issues, this study proposes a new system for evaluating bowel motility on the basis of results obtained by automatically extracting BS from the audio data recorded with a noncontact microphone. The proposed method is primarily made up of the following four steps: (1) segment detection using the short-term energy (STE) method; (2) automatic extraction of two acoustic features--mel-frequency cepstral coefficients (MFCC) [9; 10] and power-normalized cepstral coefficients (PNCC) [11; 12; 13; 14]--from segments; (3) automatic classification of segments as BS/non-BS based on an artificial neural network (ANN); and (4) evaluation of bowel motility on the basis of the acoustic features in the time domain of the BS that were automatically extracted. On the basis of audio data recorded from 20 human participants before and after they consumed carbonated water, we verified (i) the validity of automatic BS extraction by the proposed method and (ii) the validity of bowel motility evaluation based on acoustic features in the time domain.