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:  Automatic Pathology Classification 
1. Abstract of Cited Paper (BIB001):  The determination of diagnostic features in recorded heart sounds was investigated with Carpentier-Edwards (CE) bioprosthetic valves. Morphological features, extracted using the Choi-Williams distribution, achieved between 96 and 61% correct classification. The time-scale wavelet-transform feature set achieved 100% correct classification with native valve populations, and 87% with the CE replacements. 
2. Abstract of Cited Paper (BIB002):  In this paper, a feature extraction algorithm based on the wavelet packet decomposition (WPD) method was developed for the heart sound signals. Feature vectors obtained were used to classify the heart sound signals into physiological and pathological murmurs. The classification using a neural network method indicated a 85 percent accuracy. This could be an effective assistance for medical doctors to make their final diagnoses. 
3. Abstract of Cited Paper (BIB003):  Digitally recorded pathological and non-pathological phonocardiograms (PCGs) were characterised by a time-frequency (TF) method known as the trimmed mean spectrogram (TMS). Features were extracted from the TMS containing the distribution of the systolic and diastolic signatures in the TF domain. Together with the acoustic intensities in systole and diastole, these features were used as inputs to the probabilistic neural networks (PNNs) for classification. A total of 56 PCGs were employed to train the PNNs including 21 non-pathological and 35 pathological PCGs. The PNNs were then tested with a different group of 18 non-pathological and 37 pathological PCGs. The system provided a sensitivity of 97.3% (36/37) and a specificity of 94.4% (17/18) in detecting pathological systolic murmurs. The results show that the system offers a promising methodology for classifying murmurs. 
4. Abstract of Cited Paper (BIB004):  This paper proposes the use of the instantaneous energy and the frequency estimation in the classification of the heart sounds and murmurs for common heart diseases. It has been known that the present of the heart murmurs in one's heart sound indicates that there is a potential heart problem. Thus, the goal of this work is to develop a technique for detecting and classifying murmurs. Such a technique can be used as part of a heart diagnostic system. The analysis is performed based on a set of 102 data for various heart sounds. To discriminate the various heart sounds, the instantaneous energy and frequency estimation is used to estimate the features of heart sound. The techniques used to estimate the instantaneous frequency are the central finite difference frequency estimation (CFDFE) and zero crossing frequency estimation (ZCFE). From the instantaneous energy and frequency estimate, the energy and frequencies of the heart sounds are defined as the features of the heart sounds that can uniquely discriminate the various heart sounds. 
5. Abstract of Cited Paper (BIB005):  We develop a new automated pattern recognition system for interpretation of heart sounds based on wavelet decomposition of signals and classification using neural network. Inputs to the system are the heart sound signals acquired by a stethoscope in a noiseless environment. We generate features for the objective concise representation of heart sound signals by means of wavelet decomposition. Classification of the features is performed using a back propagation neural network with adaptive learning rate. Two hundred record windows obtained from young humans are studied. One hundred of the record windows in the database are selected for use as a training phase for the neural network. The test results of the intelligent pattern recognition system for ten different types of heart sound signals displayed a high success. 
6. Abstract of Cited Paper (BIB006):  A novel method is presented to determine the features of heart sounds. Feature vectors are formed by using the wavelet detail and approximation coefficients at the second and the sixth decomposition levels, respectively. Decision making is performed in four stages: Segmentation of the first and second heart sounds, normalization process, feature extraction, and classification by the artificial neural network. In this study, nine different heart sounds are classified. 
7. Abstract of Cited Paper (BIB007):  A computerized phonocardiographic telemedicine system was developed with the visual interpretation module based on the Morlet wavelet scalograms. Four studies are shown how to use this method: the special diagnostic role of PCG in 287 subjects, the feature extraction capabilities of the Morlet scalogram in 594 patients, the comparison of PCG and echocardiography method in the cardiologic diagnosis of 773 subjects, and the role of PCG in population screening and monitoring in 427 patients. The presented system is the second part - the first was the ECG - of the ongoing development of a complex vital signs measuring wireless system. 
8. Abstract of Cited Paper (BIB008):  To provide a robust representation of heart sound signal in an automatic heart disease diagnosis system, a mel-scaled wavelet transform has been developed. It combines the advantages of linear perceptual scale by mel mapping with the suitability of analyzing non-stationary signals by wavelet transform. The heart sound signal is firstly divided into windowing frames. A set of mel-scaled filterbank is then used to calculate the mel-warped spectrum, which is followed by a wavelet transform to extract the mel-scaled wavelet features. The performance of the proposed algorithm is evaluated for heart sound analysis using clean and noisy data, and compared with the standard mel-frequency cepstral coefficients. Results show that the proposed approach provides a robust representation of heart sound signal in noisy environment

Literature Review:
The vast majority of papers we have found regarding audio processing algorithms, adequate for the integration into a digital stethoscope, concern the detection of specific heart pathologies. This highlights the interest of the scientific community on this topic but, as our analysis shows, there are still some major flaws in most of them such as the absence of a clinical validation step and unconvincing experimental methodologies. Most papers use the well-established pattern recognition approach of feature extraction followed by a classifier. Due to space limitations, we will describe the most interesting papers, leaving a more detailed discussion on this topic to Section IV. Bentley (P. M. BIB001 uses Choi-Williams Distribution (CWD) as features, working with 45 normal/abnormal valve subjects. Some features were determined via visual inspection, others automatically from the CWD by simple rule-based classification. Latter (P. M. BIB002 , the authors show that CWD is a better method to represent the frequencies in PCG and to get heart sound descriptors, than other time-frequency (T-F) representations. According to them, a simple description of the T-F distribution allows an analysis of the heart valve's condition. However, they highlight the need of a more comprehensive evaluation using a larger population of test patients. Wang (P. BIB009 proposes a representation of heart sounds that is robust to noise levels of 20dB, using mel-scaled wavelet features. However, details regarding the used dataset are not clear enough for robust conclusions. Liang BIB003 developed an interesting feature vector extraction algorithm where the systolic signal is decomposed by wavelets into subbands. Then, the best basis set is selected, and the average feature vector of each heart sound recording is calculated. Neural Networks (NN) are used for classifying 20 samples after being trained with 65, obtaining an accuracy of 85%. NNs are also used by Abdel-Alim (Onsy Abdel-Alim and El-Hanjouri, 2002) for the automatic diagnostics of heart valves using wavelets feature vectors and stethoscope location information. They use two NNs: one for systolic diseases and the other for diastolic diseases. A total of 1200 cases were used: 970 cases for training and 300 for testing. The recognition rate was 95%. Turkoglu BIB006 , Ozgur (Ozgur BIB007 and El-Hanjouri (M. El-Hanjouri and Alim, 2002) also used wavelets as feature vectors for classification, although they provide too few details regarding the used data sets. Trimmed mean spectrograms are used by Leung (T.S. BIB004 to extract features of phonocardiograms. Together with the acoustic intensities in systole and diastole, the authors quantified the distinctive characteristics of different types of murmurs using NNs. One of the few papers that is conscious about the important clinical validation step is from BIB008 . The authors propose a novel sound representation (2D and 3D) and feature extraction algorithm using Morlet wavelet scalograms. After manual classification of the resulting graphs performed by two cardiologists on 773 subjects, they clinically validated the features as useful for sound and murmur extraction. BIB005 also proposes other features for classification systems based on central finite difference and zero crossing frequency estimation.