  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.
