Identification of specific Musical instruments using Machine Learning models

Abstract

Different applications including recommendation systems and digital audio workstations depend on precise recognition of instruments for their functionality along with music transcription. The research evaluates how well machine learning technologies perform in instrument classification when analyzing audio signal properties. A wide range of instrument samples from the dataset undergo processing for features that include both Mel-spectrograms and the analysis of contrasting elements. The research evaluates the performance of Support Vector Machines (SVM), Random Forest (RF), along with Convolutional Neural Networks (CNN) as models for classification purposes. Experimentally CNNs demonstrated superior performance when compared to mainstream machine learning strategies because they retain both spatial and temporal features in audio information thus achieving better classification results. Performance of the model increases to greater degrees through data augmentation along with proper tuning of model hyperparameters. The research demonstrates how machine learning techniques can identify musical instruments thus opening new possibilities for automatic music analysis systems and instant instrument detection capabilities.