One of the most important signals in the field of biomedicine is audio signals. Sound signals obtained from the body give us information about the general condition of the body. However, the detection of different sounds when recording audio signals belonging to the body or listening to them by doctors makes it difficult to diagnose the disease from these signals. In addition to isolating these sounds from the external environment, it is also necessary to separate their sounds from different parts of the body during the analysis. Separation of heart, lung and abdominal sounds will facilitate digital analysis, in particular. In this study, a dataset was created from the lungs, heart and abdominal sounds. MFCC (Mel Frekans Cepstrum Coefficient) coefficient data were obtained. The obtained coefficients were trained in the CNN (Convolution Neural Network) model. The purpose of this study is to classify audio signals. With this classification, a control system can be created. In this way, erroneous recordings that may occur when recording physicians' body voices will be prevented. When looking at the results, the educational success is about 98% and the test success is about 85%.

İnsan vücudunun durumu hakkında bilgi almak için yapılabilecek en hızlı yöntemlerden birisi vücut seslerini analiz etmektir. Seslerin dijital ortama aktarılabilmesi bu analizi kolaylaştırmaktadır. Bu çalışmada kalp, akciğer ve karın bölgelerinden alınan ses verilerinden bölge tespiti yapılmıştır. Eğitimde 12 kişiden alınan 4000 örnekleme frekansına sahip 20s lik veriler kullanılmıştır. Veriler 9 farklı saniyede incelenmiştir. Her bir saniye için tüm veriler bölünmüş ve eğitim için hazırlanmıştır. MFCC ve GTCC kullanılarak öznitelikler çıkarılmış ve bu öznitelikler CNN modelinde eğitilmiştir. MFCC ve GTCC katsayılarının sonuçlar üzerindeki etkisi kıyaslanmıştır. Eğitimde en iyi sonuç %98 ile 1,5 saniyelik kayıtlardan alınan MFCC katsayısından, validationlarda ise en iyi sonuç %85 ile 1 saniyelik kayıtların MFCC katsayılarından elde edilmiştir. Genel validation sonuçlarına bakıldığında MFCC sonuçlarının daha başarılı olduğu görülmüştür.

Diagnosis of disease with respiratory data is very important today as it was in the past. These diagnoses, which are mostly based on human experience, have begun to leave their place to machines with the development of technology. Especially with the emergence of the COVID-19 epidemic, studies on the ability of artificial intelligence to diagnose diseases by using respiratory data have increased. Sharing open-source data has paved the way for studies on this subject. Artificial intelligence makes important contributions in many fields. In the field of health, significant success results have been obtained in studies on respiratory sounds. In this article, a literature review on respiratory sounds and artificial intelligence achievements was made. Databases in literature search; IEEE, Elsevier, Pubmed and Sciencedirect. As keywords, "breathing sounds and", "respiratory sound classification", together with "artificial intelligence" and "machine learning" were chosen. In the studies, 2010 and later were discussed. In this study, artificial intelligence methods used in 35 publications selected by literature review were compared in terms of the performances obtained in the training.

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Analysis of respiratory sounds increases its importance every day. Many different methods are available in the analysis, and new techniques are continuing to be developed to further improve these methods. Features are extracted from audio signals and trained using different machine learning techniques. The use of deep learning, which is a different method and has increased in recent years, also shows its influence in this field. Deep learning techniques applied to the image of audio signals give good results and continue to be developed. In this study, image filters were applied to the values obtained from audio signals and the results of the features formed from this were examined in machine learning and deep learning techniques. Their results were compared with the results of methods that had previously achieved good results.