Download Estimating the amplitude of the cubic difference tone using a third order adaptive Volterra Filter Design method of a nonlinear filter to estimate the amplitudes of cubic difference tones is presented. To this end, a third-order Volterra filter is used to model the nonlinearity of our auditory system, and the filter coefficients are obtained using an adaptive process. The results show the filtered outputs follow very closely the experimental data as the intensity levels and the frequencies of inputs vary especially when the frequency separation between the two primary tones is not large.
Download Searching for Music Mixing Graphs: A Pruning Approach Music mixing is compositional — experts combine multiple audio processors to achieve a cohesive mix from dry source tracks. We propose a method to reverse engineer this process from the input and output audio. First, we create a mixing console that applies all available processors to every chain. Then, after the initial console parameter optimization, we alternate between removing redundant processors and fine-tuning. We achieve this through differentiable implementation of both processors and pruning. Consequently, we find a sparse mixing graph that achieves nearly identical matching quality of the full mixing console. We apply this procedure to drymix pairs from various datasets and collect graphs that also can be used to train neural networks for music mixing applications.
Download GRAFX: An Open-Source Library for Audio Processing Graphs in Pytorch We present GRAFX, an open-source library designed for handling audio processing graphs in PyTorch. Along with various library functionalities, we describe technical details on the efficient parallel computation of input graphs, signals, and processor parameters in GPU. Then, we show its example use under a music mixing scenario, where parameters of every differentiable processor in a large graph are optimized via gradient descent. The code is available at https://github.com/sh-lee97/grafx.