Paper Archive

Deep learning models have seen widespread use in modelling LFOdriven audio effects, such as phaser and flanger. Although existing neural architectures exhibit high-quality emulation of individual effects, they do not possess the capability to manipulate the output via control parameters. To address this issue, we introduce Controllable Neural Frame-based Modulation Effects (CONMOD), a single black-box model which emulates various LFOdriven effects in a frame-wise manner, offering control over LFO frequency and feedback parameters. Additionally, the model is capable of learning the continuous embedding space of two distinct phaser effects, enabling us to steer between effects and achieve creative outputs. Our model outperforms previous work while possessing both controllability and universality, presenting opportunities to enhance creativity in modern LFO-driven audio effects. Additional demo of our model is available in the accompanying website.1

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Disc jockeys (DJs) use audio effects to make a smooth transition from one song to another. There have been attempts to computationally analyze the creative process of seamless mixing. However, only a few studies estimated fader or equalizer (EQ) gains controlled by DJs. In this study, we propose a method that jointly estimates time-varying fader and EQ gains so as to reproduce the mix from individual source tracks. The method approximates the equalizer filters with a linear combination of a fixed equalizer filter and a constant gain to convert the joint estimation into a convex optimization problem. For the experiment, we collected a new DJ mix dataset that consists of 5,040 real-world DJ mixes with 50,742 transitions, and evaluated the proposed method with a mix reconstruction error. The result shows that the proposed method estimates the time-varying fader and equalizer gains more accurately than existing methods and simple baselines.

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