This work aims to examine one of the cornerstone problems of Musical Instrument Recognition, in particular instrument classification. IRMAS (Instrument recognition in Musical Audio Signals) data set is chosen. The data includes music obtained from various decades in the last century, thus having a wide variety in audio quality. We have presented a very concise summary of past work in this domain. Having implemented various supervised learning algorithms for this classification task, SVM classifier has outperformed the other state-of-the-art models with an accuracy of 79%. The classifier had a major challenge distinguishing between flute and organ. We also implemented Unsupervised techniques out of which Hierarchical Clustering has performed well. We have included most of the code (jupyter notebook) for easy reproducibility.
翻译:这项工作旨在研究音乐仪器识别,特别是仪器分类的一个基本问题。 选择了 IRMAS (音乐音频信号中的仪器识别) 数据集。 数据包括上个世纪数十年来获得的音乐, 因而具有广泛的音频质量。 我们已经对该领域过去的工作作了非常简洁的总结。 在为这一分类任务实施了各种监督的学习算法之后, SVM 分类器的精确度超过了其他最先进的模型79%。 分类器在区分笛子和器官方面面临着重大挑战。 我们还应用了不受监督的技术, 高射层集群在其中表现良好。 我们把大部分代码( Jupyter 笔记本) 包括了容易复制的代码( jupyter 笔记本 ) 。