Musical Instrument Classification using Audio Features and Convolutional Neural Network
Abstract
The classification of acoustic instruments is the subject of this research, which utilizes Convolutional Neural Networks (CNNs). We employ a dataset from Kaggle that includes audio recordings of the piano, violin, drums, and guitar. In the training set, the dataset comprises 700 samples of guitar, percussion, and violin and 528 samples of piano. The test set contains 80 samples of each instrument. Mel spectrograms, MFCCs, and other spectral and non-spectral characteristics are among the features that can be extracted using the librosa package. Three feature sets—spectral-only, non-spectral-only, and a combined set—are employed to evaluate the efficacy of CNN models—various CNNs configurations by adjusting the number of convolutional filters, learning rates, and epochs. The combined feature set achieves the highest performance, with a validation accuracy of 71.8% and a training accuracy of 76.9%. In contrast, non-spectral features achieve 68.4% validation accuracy, while spectral-only features achieve 69.3%. These findings demonstrate the advantages of employing a vast feature set for precise classification.
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