论文标题
源分离和深度可分开的卷积
Source Separation and Depthwise Separable Convolutions for Computer Audition
论文作者
论文摘要
鉴于深度音乐源分离的最新进展，我们提出了一种功能表示方法，该方法将源分离与最先进的表示学习技术相结合，该技术适当地重新用于计算机试听（即机器聆听）。我们在充满挑战的电子舞蹈音乐（EDM）数据集上训练深度可分离的卷积神经网络，并将其性能与在源分离和标准频谱图上运行的卷积神经网络进行比较。结果表明，与标准的单光谱方法相比，源分离可以改善有限数据设置的分类性能。
Given recent advances in deep music source separation, we propose a feature representation method that combines source separation with a state-of-the-art representation learning technique that is suitably repurposed for computer audition (i.e. machine listening). We train a depthwise separable convolutional neural network on a challenging electronic dance music (EDM) data set and compare its performance to convolutional neural networks operating on both source separated and standard spectrograms. It is shown that source separation improves classification performance in a limited-data setting compared to the standard single spectrogram approach.
