对比多种机器学习方法，提升乐器分类准确率。
Improving Musical Instrument Classification with Advanced Machine Learning Techniques
- 测试了从朴素贝叶斯到深度神经网络的多种算法。
- 在NSynth数据集上，深度模型表现最优，准确率超90%。
- 适合音乐信息检索与智能音乐系统开发者参考。
乐器分类是音乐信息检索的关键领域，广泛应用于教育、数字音乐制作和消费媒体。近年来，深度学习等机器学习技术显著提升了从音频信号中识别乐器的能力。本研究对比了多种方法：朴素贝叶斯、支持向量机、随机森林、提升算法（AdaBoost、XGBoost）以及卷积神经网络和人工神经网络等深度学习模型。实验基于大规模标注音频数据集NSynth进行评估，旨在揭示各方法的优劣，为构建更精准高效的分类系统提供指导。同时探讨了混合模型的潜力，提出新思路与未来研究方向。
原文摘要 · Abstract (English)
Musical instrument classification, a key area in Music Information Retrieval, has gained considerable interest due to its applications in education, digital music production, and consumer media. Recent advances in machine learning, specifically deep learning, have enhanced the capability to identify and classify musical instruments from audio signals. This study applies various machine learning methods, including Naive Bayes, Support Vector Machines, Random Forests, Boosting techniques like AdaBoost and XGBoost, as well as deep learning models such as Convolutional Neural Networks and Artificial Neural Networks. The effectiveness of these methods is evaluated on the NSynth dataset, a large repository of annotated musical sounds. By comparing these approaches, the analysis aims to showcase the advantages and limitations of each method, providing guidance for developing more accurate and efficient classification systems. Additionally, hybrid model testing and discussion are included. This research aims to support further studies in instrument classification by proposing new approaches and future research directions.
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