基于多核深度卷积网络的音高信息乐器分配
Pitch-Informed Instrument Assignment Using a Deep Convolutional Network with Multiple Kernel Shapes
摘要：
本文提出了一种用于执行音符级别乐器分配的深度卷积神经网络。给定复调多乐器音乐信号及其地面实况或预测音符，目标是为每个音符分配乐器源。这个问题是解决作为一个音高通知分类任务，每个音符进行单独分析。我们还建议在卷积层中使用几种内核形状，以促进有效的音色区分特征图的学习。在MusicNet数据集上使用7个乐器类的实验表明，当原始多音高注释用作系统的音高信息时，我们的方法能够实现平均F分数为0.904，并且如果使用第三方多音高估计算法提供音符信息，则它也表现出色。我们还包括消融研究调查使用多个内核形状的影响，并比较不同的输入表示的音频和笔记相关的信息。
英文摘要：
This paper proposes a deep convolutional neural network for performing note-level instrument assignment. Given a polyphonic multi-instrumental music signal along with its ground truth or predicted notes, the objective is to assign an instrumental source for each note. This problem is addressed as a pitch-informed classification task where each note is analysed individually. We also propose to utilise several kernel shapes in the convolutional layers in order to facilitate learning of efficient timbre-discriminative feature maps. Experiments on the MusicNet dataset using 7 instrument classes show that our approach is able to achieve an average F-score of 0.904 when the original multi-pitch annotations are used as the pitch information for the system, and that it also excels if the note information is provided using third-party multi-pitch estimation algorithms. We also include ablation studies investigating the effects of the use of multiple kernel shapes and comparing different input representations for the audio and the note-related information.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
