Person Identification Using Bronchial Breath Sounds Recorded by Mobile Devices

Abstract

This study examines the use of breath sounds intrusively recorded by mobile devices for person identification (PID), which is referred to as mobile-sensed BreathPID. A custom dataset of breath sounds from 21 volunteers is prepared for investigation and analysis. To overcome the problem of spare training data, we incorporate various audio data augmentation (DA) methods with the self-supervised learning (SSL) approach to train the BreathPID’s learning models. SSL-based models for BreathPID are developed in two phases: firstly, solving proposed pretext task(s) without identity information to effectively learn core characteristics of data; then further finetuning the models on the labeled data for the downstream BreathPID task. Several types of pretext or auxiliary tasks are investigated. First, when considering each DA technique, the pretext task is defined as the detection of augmentation levels, for instance, the levels of noise added to original data samples. When utilizing multiple DA techniques, the identification of DA types is defined as the pretext task. In addition, various issues in developing robust BreathPID systems are taken into consideration, including network design, changes in input length, and the ability of noise resistance. From the experimental results, we find that SSL-based BreathPID with the combined use of four DA techniques (i.e., noise addition, speed changing, time shifting, and spectrogram masking) achieves promising results which are higher than those of SSL-based models using single DA technique and those of typical supervised models. Also, the proposed system shows good resistance to noise effects and changes in the input size. Mobile-sensed BreathPID achieves the equivalent or superior results compared to stethoscope-sensed BreathPID (where breath sounds are sensed using specialty stethoscopes). The proposed approach can be applied to the authentication function or health monitoring applications on mobile devices.

Keywords

• Audio-based identification
• breath sounds
• biometrics
• data augmentation
• mobile devices
• neural networks