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arXiv · 2303.08610

Blind Estimation of Audio Processing Graph

Sungho Lee·Jaehyun Park·Seungryeol Paik·Kyogu Lee
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Abstract

Musicians and audio engineers sculpt and transform their sounds by connecting multiple processors, forming an audio processing graph. However, most deep-learning methods overlook this real-world practice and assume fixed graph settings. To bridge this gap, we develop a system that reconstructs the entire graph from a given reference audio. We first generate a realistic graph-reference pair dataset and train a simple blind estimation system composed of a convolutional reference encoder and a transformer-based graph decoder. We apply our model to singing voice effects and drum mixing estimation tasks. Evaluation results show that our method can reconstruct complex signal routings, including multi-band processing and sidechaining.

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Sungho Lee, Jaehyun Park, Seungryeol Paik, Kyogu Lee. 2023-03-15. Blind Estimation of Audio Processing Graph. https://arxiv.org/abs/2303.08610

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