论文标题
吉他效应识别和参数估计与卷积神经网络
Guitar Effects Recognition and Parameter Estimation with Convolutional Neural Networks
论文作者
论文摘要
尽管吉他效果很普遍，但关于吉他录音中特定插件或效果单元的分类和参数估计的现有研究很少。在本文中，卷积神经网络用于13个超速，失真和模糊吉他效应的分类和参数估计。组装了一个新型的加工电吉他样品数据集，其中四个子数据集由单声音或多音样品组成，以及离散或连续设置值，总计约250小时的处理样品。比较了在相同或不同的子数据库上训练和测试的网络的结果。我们发现，离散数据集可能会导致连续性的数据集同样高性能，同时更易于设计，分析和修改。分类精度高于80 \％，混乱矩阵反映了效果音色和电路设计的相似性。在0.0至1.0之间的参数值以下，在大多数情况下，平均绝对误差低于0.05，而根平方误差在所有情况下都低于0.1。
Despite the popularity of guitar effects, there is very little existing research on classification and parameter estimation of specific plugins or effect units from guitar recordings. In this paper, convolutional neural networks were used for classification and parameter estimation for 13 overdrive, distortion and fuzz guitar effects. A novel dataset of processed electric guitar samples was assembled, with four sub-datasets consisting of monophonic or polyphonic samples and discrete or continuous settings values, for a total of about 250 hours of processed samples. Results were compared for networks trained and tested on the same or on a different sub-dataset. We found that discrete datasets could lead to equally high performance as continuous ones, whilst being easier to design, analyse and modify. Classification accuracy was above 80\%, with confusion matrices reflecting similarities in the effects timbre and circuits design. With parameter values between 0.0 and 1.0, the mean absolute error is in most cases below 0.05, while the root mean square error is below 0.1 in all cases but one.
