End-to-End Amp Modeling: From Data to Controllable Guitar Amplifier Models

Autor:	Juvela, Lauri, Damskägg, Eero-Pekka, Peussa, Aleksi, Mäkinen, Jaakko, Sherson, Thomas, Mimilakis, Stylianos I., Gotsopoulos, Athanasios
Rok vydání:	2024
Předmět:	Computer Science - Sound Electrical Engineering and Systems Science - Audio and Speech Processing
Druh dokumentu:	Working Paper
Popis:	This paper describes a data-driven approach to creating real-time neural network models of guitar amplifiers, recreating the amplifiers' sonic response to arbitrary inputs at the full range of controls present on the physical device. While the focus on the paper is on the data collection pipeline, we demonstrate the effectiveness of this conditioned black-box approach by training an LSTM model to the task, and comparing its performance to an offline white-box SPICE circuit simulation. Our listening test results demonstrate that the neural amplifier modeling approach can match the subjective performance of a high-quality SPICE model, all while using an automated, non-intrusive data collection process, and an end-to-end trainable, real-time feasible neural network model. Comment: Presented at ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2403.08559 Zobrazit plný text záznamu View this record from Arxiv