End-to-end equalization with convolutional neural networks
End-to-end equalization with convolutional neural networks
复制标题
使用卷积神经网络进行端到端均衡
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
J. Reiss
中科院分区：
文献类型：
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作者：
M. M. Ramírez;J. Reiss
这项工作旨在实现一种新的深度学习架构，以在匹配均衡的背景下执行音频处理。大多数现有的自动和匹配均衡方法显示出有效的性能，其目标是找到给定频率响应的相应传递函数。然而，这些过程需要对要建模的滤波器类型的先验知识。此外，在自动混合环境中需要固定滤波器组架构。基于端到端卷积神经网络，我们介绍了一种通用的均衡匹配架构。因此，通过使用端到端学习方法，该模型将均衡目标近似为基于内容的变换，而不直接找到传递函数。网络学习如何直接处理音频，以便匹配均衡的目标音频。我们通过无监督和有监督的学习过程来训练网络。我们分析模型实际上在学习什么，以及给定的任务是如何完成的。我们展示了该模型执行匹配均衡搁置，峰值，低通和高通IIR和FIR均衡器。
This work aims to implement a novel deep learning architecture to perform audio processing in the context of matched equalization. Most existing methods for automatic and matched equalization show effective performance and their goal is to find a respective transfer function given a frequency response. Nevertheless, these procedures require a prior knowledge of the type of filters to be modeled. In addition, fixed filter bank architectures are required in automatic mixing contexts. Based on end-to-end convolutional neural networks, we introduce a general purpose architecture for equalization matching. Thus, by using an end-toend learning approach, the model approximates the equalization target as a content-based transformation without directly finding the transfer function. The network learns how to process the audio directly in order to match the equalized target audio. We train the network through unsupervised and supervised learning procedures. We analyze what the model is actually learning and how the given task is accomplished. We show the model performing matched equalization for shelving, peaking, lowpass and highpass IIR and FIR equalizers.