End-to-end equalization with convolutional neural networks
MetadataShow full item record
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. Neverthe-less, these procedures require a prior knowledge of the type offilters to be modeled. In addition, fixed filter bank architecturesare required in automatic mixing contexts. Based on end-to-endconvolutional neural networks, we introduce a general purpose ar-chitecture for equalization matching. Thus, by using an end-to-end learning approach, the model approximates the equalizationtarget as a content-based transformation without directly findingthe transfer function. The network learns how to process the au-dio directly in order to match the equalized target audio. We trainthe network through unsupervised and supervised learning proce-dures. We analyze what the model is actually learning and howthe given task is accomplished. We show the model performing matched equalization forshelving, peaking, lowpass and highpass IIR and FIR equalizers.