论文标题
神经循环组合器：评估循环兼容性的神经网络模型
Neural Loop Combiner: Neural Network Models for Assessing the Compatibility of Loops
论文作者
论文摘要
使用循环的音乐制作人可能可以在循环库中访问数千个，但是找到兼容的音乐库是一个耗时的过程。我们希望通过自动化减轻这种负担。用于估计兼容性的最先进的系统，例如Automashupper，主要是基于规则的，并且可以通过机器学习来进行改进。要训练模型，我们需要大量具有地面真理兼容性值的循环。没有这样的数据集，因此我们从现有音乐中提取循环以获得兼容循环的积极示例，并提出和比较选择负面示例的各种策略。为了重新可调性，我们从免费的音乐存档中策划数据。使用此数据，我们研究了两种类型的模型体系结构，以估算循环的兼容性：一个基于暹罗网络，另一个基于纯卷积神经网络（CNN）。我们进行了一项用户研究，其中参与者对每个模型建议的组合质量进行了评分，并发现CNN胜过暹罗网络。两种基于模型的方法都优于基于规则的方法。我们已经打开了用于构建模型和数据集的代码。
Music producers who use loops may have access to thousands in loop libraries, but finding ones that are compatible is a time-consuming process; we hope to reduce this burden with automation. State-of-the-art systems for estimating compatibility, such as AutoMashUpper, are mostly rule-based and could be improved on with machine learn-ing. To train a model, we need a large set of loops with ground truth compatibility values. No such dataset exists, so we extract loops from existing music to obtain positive examples of compatible loops, and propose and compare various strategies for choosing negative examples. For re-producibility, we curate data from the Free Music Archive.Using this data, we investigate two types of model architectures for estimating the compatibility of loops: one based on a Siamese network, and the other a pure convolutional neural network (CNN). We conducted a user study in which participants rated the quality of the combinations suggested by each model, and found the CNN to outperform the Siamese network. Both model-based approaches outperformed the rule-based one. We have opened source the code for building the models and the dataset.
