论文标题
XGBoost识别乐器识别功能融合
Musical Instrument Recognition by XGBoost Combining Feature Fusion
论文作者
论文摘要
乐器分类是音乐信息检索的重点之一（MIR）。为了解决当前乐器分类模型性能不佳的问题，我们提出了一种基于多通道功能融合和XGBoost的乐器分类算法。基于音频功能提取和数据集的融合，这些功能被输入到XGBoost模型中以进行训练；其次，我们通过将不同的功能组合和几种经典的机器学习模型（例如天真的贝叶斯）组合来验证乐器分类任务中算法的出色性能。该算法在Medley-Solos-DB数据集上达到了97.65％的精度，表现优于现有模型。该实验为乐器分类的功能工程中的特征选择提供了参考。
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.
