You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A SURVEY OF AUDIO PROCESSING ALGORITHMS FOR DIGITAL STETHOSCOPES 
Chapter Title:  Heart Sound Segmentation 
1. Abstract of Cited Paper (BIB001):  A heart sound segmentation algorithm, which separates the heart sound signal into four parts (the first heart sound, the systolic period, the second heart sound and the diastolic period), has been developed. The algorithm uses discrete wavelet decomposition and reconstruction to produce intensity envelopes of approximations and details of the original phonocardiographic signal. The performance of the algorithm has been evaluated using 1165 cardiac periods from 77 digital phonocardiographic recordings including normal and abnormal heart sounds. The algorithm has been shown to perform correctly in over 93% of cases. 
2. Abstract of Cited Paper (BIB002):  An algorithm based on the wavelet decomposition and reconstruction method was developed to extract features from the heart sound recordings. An artificial neural networks classification method based on these features was used to classify the heart sound signals into physiological and pathological murmurs. The classification result indicated 74.4% accuracy. 
3. Abstract of Cited Paper (BIB003):  A heart sound segmentation and feature extraction algorithm, which separates the heart sound signal into four parts: the first heart sound S1, the systolic period, the second heart sound S2, and the diastolic period. A features extraction algorithm has been developed. The algorithm uses discrete wavelet decomposition and reconstruction to produce approximations and details of the original phonocardiographic signal

Literature Review:
In HSS we expect to identify and segment the four main constituents of a cardiac cycle. This is typically accomplished by identifying the position and duration of S1 and S2, using some sort of peak-picking methodology on a pre-processed signal. BIB001 has used discrete wavelet decomposition and reconstructed the signal using only the most relevant frequency bands. Peak-picking was performed by thresholding the normalized average Shannon energy, and discarding extra peaks via analysis of the mean and variance of peak intervals. Finally, they distinguish between S1 and S2 peaks (assuming that the diastolic period is longer than the systolic one, and that the later is more constant), and estimate their durations. A classification accuracy of 93% was obtained on 515 periods of PCG signal recordings from 37 digital phonocardiographic recordings. The same authors further improved the statistical significance of their results by obtaining the same accuracy using 1165 cardiac periods from 77 recordings (H. BIB002 , and later attempted murmur classification based on these features and neural network classifiers, obtaining 74% accuracy BIB003 . Omran BIB004 has also studied this problem using normalized Shannon entropy after wavelet decomposition of the audio signal, but their experimental methodology is not so convincing.