Lung auscultation has long been used as a valuable medical tool to assess respiratory health and has gotten a lot of attention in recent years, notably following the coronavirus epidemic. Lung auscultation is used to assess a patient’s respiratory role. Modern technological progress has guided the growth of computer-based respiratory speech investigation, a valuable tool for detecting lung abnormalities and diseases. Several recent studies have reviewed this important area, but none are specific to lung sound-based analysis with deep-learning architectures from one side and the provided information was not sufficient for a good understanding of these techniques. This paper gives a complete review of prior deep-learning-based architecture lung sound analysis. Deep-learning-based respiratory sound analysis articles are found in different databases including the Plos, ACM Digital Libraries, Elsevier, PubMed, MDPI, Springer, and IEEE. More than 160 publications were extracted and submitted for assessment. This paper discusses different trends in pathology/lung sound, the common features for classifying lung sounds, several considered datasets, classification methods, signal processing techniques, and some statistical information based on previous study findings. Finally, the assessment concludes with a discussion of potential future improvements and recommendations.

Audio command recognition methods are essential to be recognized for performing user instructions, especially for people with disabilities. Previous studies couldn’t examine and classify the performance optimization of up to twelve audio commands categories. This work develops a microphone-based audio commands classifier using a convolutional neural network (CNN) with performance optimization to categorize twelve classes including background noise and unknown words. The methodology mainly includes preparing the input audio commands for training, extracting features, and visualizing auditory spectrograms. Then a CNN-based classifier is developed and the trained architecture is evaluated. The work considers minimizing latency by optimizing the processing phase by compiling MATLAB code into C code if the processing phase reaches a peak algorithmically. In addition, the method conducts decreasing the frame size and increases the sample rate that is also contributed to minimizing latency and maximizing the performance of processing audio input data. A modest bit of dropout to the input to the final fully connected layer is added to lessen the likelihood that the network will memorize particular elements of the training data. We explored expanding the network depth by including convolutional identical elements, ReLu, and batch normalization layers to improve the network's accuracy. The training progress demonstrated how fast the accuracy of the network is increasing to reach about 98.1 %, which interprets the ability of the network to over-fit the data of training. This work is essential to serve speech and speaker recognition such as smart homes and smart wheelchairs, especially for people with disabilities