Deep learning can fool listeners by imitating any guitar amplifier | Machine learning & AI - Tech Xplore

A study from the Aalto Acoustics Lab demonstrates that digital simulations of guitar amplifiers can sound just like the real thing by Mark Fletcher, Annamari Tolonen, Aalto University.

Testing guitar signals at the Acoustics Lab.
Photo: Aalto University

The implications are that as the software models continue to improve, they can replace traditional analogue guitar amplifiers, which are bulky, fragile and expensive.

Many popular guitar amplifiers and distortion effects are based on analogue circuitry. To achieve the desired distortion of the guitar signal, these circuits use nonlinear components, such as vacuum tubes, diodes, or transistors. As music production becomes increasingly digitized, the demand for faithful digital emulations of analogue audio effects is increasing.

Professor Vesa Välimäki explains that this is an exciting development in deep learning, 'Deep neural networks for guitar distortion modeling has been tested before, but this is the first time, where blind-test listeners couldn't tell the difference between a recording and a fake distorted guitar sound! This is akin to when the computer first learned to play chess'.

The main objective of the field of Virtual Analog (VA) modeling is to create digital emulations of these analogue systems which will allow bulky, expensive and fragile analogue equipment to be replaced by software plugins that can be used on a modern desktop or laptop computer...

Alec Wright, a doctoral student, focusing on audio processing using deep learning says,' The tests were conducted to validate the performance of models emulating either the Blackstar HT5 Metal or Mesa Boogie Express 5:50+ tube amplifiers. The models were created with a focus on real-time performance, and all of them can be run in real-time on a desktop computer'.  
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Source: Tech Xplore