Musical Instrument Recognition by XGBoost Combining Feature Fusion
release_tcdzmjnizng5pmfv6kv643yyle
by
Yijie Liu, Yanfang Yin, Qigang Zhu, Wenzhuo Cui
2022
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.
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