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arXiv · 2607.10086

WaveNet-Style Guitar Amplifier Model Pruning for Real-Time iOS Deployment

Ryota Sato·Eli Silverstein
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Abstract

WaveNet-style convolutional networks emulate tube amplifiers and distortion pedals with high fidelity, but their computational cost has confined them to desktops or dedicated DSP hardware. We present a sparse-enabled WaveNet inference engine for iOS that runs heavily pruned neural guitar amplifier models in real time on iPhones. Aggressive iterative magnitude pruning removes 90% of the network weights with no perceptible loss in quality. A custom sparse C++ engine turns this sparsity directly into compute savings, sustaining low-latency real-time operation on a CPU-only iPhone implementation where the dense model cannot. On-device output matches the trained model to within int16 quantization error. At the demonstration, visitors will play a guitar through the app on iPhone hardware and A/B the on-device pruned model against the physical pedal it emulates. Source code and audio examples are available at https://github.com/ryos17/wavenet-imp.

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BibTeXRIS

Ryota Sato, Eli Silverstein. 2026-07-11. WaveNet-Style Guitar Amplifier Model Pruning for Real-Time iOS Deployment. https://arxiv.org/abs/2607.10086

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