Enhancing machine learning models with audio augmentation techniques

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This paper investigates how audio augmentation techniques improve the classification accuracy of guitar chords. By applying methods such as noise addition, speed modification, and time shifting, a CNN trained on an augmented dataset achieved better results compared to non-augmented data. The findings highlight the importance of task-specific augmentation techniques in enhancing audio analysis