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Musical Instrument Recognition by XGBoost Combining Feature Fusion (2206.00901v1)

Published 2 Jun 2022 in cs.SD and eess.AS

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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Authors (4)
  1. Yijie Liu (5 papers)
  2. Yanfang Yin (1 paper)
  3. Qigang Zhu (1 paper)
  4. Wenzhuo Cui (1 paper)
Citations (1)

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