Abstract
Classification of audio files using CNN (Convolutional Neural Network) algorithm is an important application in the field of audio processing and artificial intelligence. This process aims to automatically classify audio files into different classes and can be used in speech recognition, emotional analysis, voice-based control systems and many other applications. The aim of this study is to perform spectrum transformation of instrumental sounds and classify them using image classification algorithms. The dataset contains a total of 1500 data from five different instruments. Audio files were processed, and signal and spectrogram images of each audio file were obtained. DenseNet121, ResNet and CNN algorithms were tested in experimental studies. The most successful results belong to the CNN algorithm with 99.34%.
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