No abstract

In this research, different audio feature extraction techniques are implemented and classification approaches are presented to classify seven types of wind. We applied features techniques such as Zero Crossing Rate (ZCR), Fast Fourier Transformation (FFT), Linear predictive coding (LPC), and Perceptual Linear Prediction (PLP). We know that some of these methods are good with human voices, but we tried to apply them here to characterize the wind audio content. The CNN classification method is implemented to determine the class of input wind sound signal. Experimental results show that each of these extraction feature methods give different results, but classification accuracy that are obtained by using PLP features return the best results. Povzetek: V tej raziskavi se izvajajo različne tehnike ekstrakcije zvočnih funkcij in predstavljeni so klasifikacijski pristopi za razvrščanje sedmih vrst vetra. Kjer smo uporabili tehniko funkcij, kot so Zero Crossing Rate (ZCR), Fast Fourier Transformation (FFT), Linear Prediction Coding (LPC), Perceptual Linear Prediction (PLP). Vemo, da nekatere od teh metod dobro vplivajo na človeške glasove, vendar smo jih poskušali uporabiti tukaj za označevanje zvočne vsebine vetra. Za določitev razreda vhodnega zvočnega signala vetra je uporabljena klasifikacijska metoda CNN. Eksperimentalni rezultati kažejo, da je vsaka od teh metod ekstrakcijskih lastnosti dala različne rezultate, vendar se je za klasifikacijo lastnosti PLP izkazalo, da imajo najboljše rezultat.

Image processing and computer vision have a major role in addressing many problems, where images and techniques that are dealt with them contribute greatly to finding solutions to many topics and in different directions. Classification techniques have a large and important role in this field, through which it is possible to recognize and classify images in a way that helps in solving a specific problem. Among the most prominent models that are distinguished for their ability and accuracy in distinguishing is the CNN model. In this research, we have introduced a system to classify the sea coral images because sea coral and its classes have many benefits in many aspects of our lives. The important thing in this work is to study four CNN architectures model (i.e., AlexNet, SqueezeNet, to determine the accuracy and efficiency of these architectures and determine the best of them with coral image data, and we are shown the details in the research paragraphs. The results showed 83.33% accuracy for AlexNet, 80.85% SqueezeNet, 90.5% GoogLeNet and 93.17% for Inception-v3.Povzetek: Predstavljena je uporaba arhitektur konvolucijskih nevronskih mrež (CNN) za razvrščanje slik morskih koral.