Acoustic signal classification issues are addressed in this work using spectral examination, channel extracting the features from the input and machine learning algorithm. This brief article examines the effect of various settings on feature extraction. This feature-level channel combination's accuracy increase is then observed. To categorise things, pattern recognition utilises a variety of classification schemes. "Pattern" refers to the measures that must be categorised with accurate feature extracted. Images and audio signals are among the most common kinds of measurements. The proposed Support Vector Machine (SVM) is used for the necessity of an effective categorization of acoustic signals driven by the continual improvements in multimedia technology. This study uses two machine learning algorithms to enhance audio classification and categorization. The proposed SVM achieves superior performance than the other ML algorithm by spectral features.

Deep learning algorithms are very effective in the application of classification and prediction over the traditional estimators. The proposed work employs a bottleneck layer algorithm on CICIDS-2017 dataset to prove its efficacy on the prediction of cyber-attacks. The performance of the bottleneck model architecture is incorporated with Artificial Neural Network (ANN) and Deep Neural Network (DNN) models and compared over the traditional ANN, DNN and Support Vector Machines (SVM) models. The experimental work reaches a maximum accuracy of 92.35% in the DNN and 90.98% in ANN algorithm respectively.