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.

翻译：乐器分类作为音乐信息检索的关键领域，因其在教育、数字音乐制作和消费者媒体中的应用而受到广泛关注。近年来，机器学习特别是深度学习的进展，增强了从音频信号中识别和分类乐器的能力。本研究应用了多种机器学习方法，包括朴素贝叶斯、支持向量机、随机森林、AdaBoost和XGBoost等提升技术，以及卷积神经网络和人工神经网络等深度学习模型。这些方法的有效性在NSynth数据集（一个大型标注音乐声音库）上进行了评估。通过比较这些方法，本分析旨在展示每种方法的优势与局限性，为开发更准确高效的分类系统提供指导。此外，研究还包含了混合模型测试与讨论。本研究旨在通过提出新方法和未来研究方向，支持乐器分类领域的进一步研究。