Vücut Seslerinden Bölge Tanımlanması için İdeal Kayıt Süresinin Belirlenmesinde MFCC ve GTCC Özniteliklerinin Etkisinin Karşılaştırılması

Year 2022, Issue: 43, 36 - 40, 30.11.2022

Osman Balli

https://doi.org/10.31590/ejosat.1202062

Abstract

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.

Keywords

Artificial intelligence, body sounds, convolution nneural network, gammatone cepstral coefficients, mel-frequency cestrum coefficients

Comparison of the Effect of MFCC and GTCC Features on Determining the Ideal Recording Time for Body Sound Location Identification

Abstract

One of the fastest ways to get information about the state of the human body is to analyse body sounds. The ability to transfer sounds to a digital medium facilitates this analysis. In this study, zone detection was performed from the sound data obtained from the heart, lung, and abdominal regions. 20s data with a sampling frequency of 4000 from 12 men were used in the training. The data was analysed in 9 different seconds. All data for each second is divided and prepared for training. Features were extracted using MFCC and GTCC and these features were trained in CNN model. The effect of MFCC and GTCC coefficients on the results was compared. In training, the best result was obtained from the MFCC coefficient obtained from 1.5-second recordings with 98%, and in validations, the best result was obtained from MFCC coefficients of 1-second recordings with 85%. Looking at the general validation results, it was seen that the MFCC results were more successful.

Keywords

Artificial intelligence, body sounds, convolution nneural network, gammatone cepstral coefficients, mel-frequency cestrum coefficients

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