Abstract
Analog audio effects and synthesizers often owe their distinct sound to circuit nonlinearities. Faithfully modeling such significant aspect of the original sound in virtual analog software can prove challenging. The current work proposes a generic data-driven approach to virtual analog modeling and applies it to the Fender Bassman 56F-A vacuum-tube amplifier. Specifically, a feedforward variant of the WaveNet deep neural network is trained to carry out a regression on audio waveform samples from input to output of a SPICE model of the tube amplifier. The output signals are pre-emphasized to assist the model at learning the high-frequency content. The results of a listening test suggest that the proposed model accurately emulates the reference device. In particular, the model responds to user control changes, and faithfully restitutes the range of sonic characteristics found across the configurations of the original device.
Original language | English |
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Title of host publication | 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019; Brighton; United Kingdom; 12-17 May 2019 : Proceedings |
Publisher | IEEE |
Pages | 471-475 |
Number of pages | 5 |
ISBN (Electronic) | 978-1-4799-8131-1 |
ISBN (Print) | 978-1-4799-8132-8 |
DOIs | |
Publication status | Published - 1 May 2019 |
MoE publication type | A4 Article in a conference publication |
Event | IEEE International Conference on Acoustics, Speech, and Signal Processing - Brighton, United Kingdom Duration: 12 May 2019 → 17 May 2019 Conference number: 44 |
Publication series
Name | Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing |
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ISSN (Print) | 1520-6149 |
ISSN (Electronic) | 2379-190X |
Conference
Conference | IEEE International Conference on Acoustics, Speech, and Signal Processing |
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Abbreviated title | ICASSP |
Country/Territory | United Kingdom |
City | Brighton |
Period | 12/05/2019 → 17/05/2019 |
Keywords
- Integrated circuit modeling
- Neural networks
- Convolution
- Computational modeling
- Predictive models
- SPICE
- Computer architecture
- Audio systems
- feedforward neural networks
- music
- nonlinear systems
- supervised learning
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Aalto Acoustics Lab
Ville Pulkki (Manager)
School of Electrical EngineeringFacility/equipment: Facility
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