Musical Instrument Recognition by XGBoost Combining Feature Fusion

TL;DR

This paper introduces a novel musical instrument classification method using multi-channel feature fusion and XGBoost, achieving high accuracy and outperforming existing models in the field.

Contribution

The paper presents a new classification algorithm combining feature fusion and XGBoost, demonstrating superior performance over classical models.

Findings

Achieved 97.65% accuracy on Medley-solos-DB dataset.

Verified the effectiveness of feature fusion and XGBoost in instrument classification.

Provided insights into feature selection for musical instrument recognition.

Abstract

Musical instrument classification is one of the focuses of Music Information Retrieval (MIR). In order to solve the problem of poor performance of current musical instrument classification models, we propose a musical instrument classification algorithm based on multi-channel feature fusion and XGBoost. Based on audio feature extraction and fusion of the dataset, the features are input into the XGBoost model for training; secondly, we verified the superior performance of the algorithm in the musical instrument classification task by com-paring different feature combinations and several classical machine learning models such as Naive Bayes. The algorithm achieves an accuracy of 97.65% on the Medley-solos-DB dataset, outperforming existing models. The experiments provide a reference for feature selection in feature engineering for musical instrument classification.