{'fraction_non_alphanumeric': 0.03879310344827586, 'fraction_numerical': 0.0, 'mean_word_length': 5.781021897810219, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A Computer-based Sound Recognition System for the Diagnosis of Pulmonary Disorders

paper presents a computer-based sound recognition system for diagnosis of pulmonary disorders based on the interpretation of the lung sound signals (LSS). We propose a novel method of analysis of LSS using the Mel-frequency cepstral coefficients, the spectral and temporal parameters estimated from the frequency subbands of the discrete wavelet transform. A Linde Buzo Gray (LBG) clustering neural network model is developed for classifying the LSS to one of the six categories: normal, wheeze, crackle, squawk, stridor, or rhonchus. Experimental results demonstrate the effectiveness of the proposed system in detecting pulmonary disorders. KeywordsSound Signals (LSS), Discrete Wavelet Transform (DWT), Mel-Frequency Cepstral Coefficients (MFCC), Spectral and temporal parameters, Linde Buzo Gray (LBG), Multi-Layer Perception (MLP) Network.