Download Neural Audio Processing on Android Phones This study investigates the potential of real-time inference of neural audio effects on Android smartphones, marking an initial step towards bridging the gap in neural audio processing for mobile devices. Focusing exclusively on processing rather than synthesis, we explore the performance of three open-source neural models across five Android phones released between 2014 and 2022, showcasing varied capabilities due to their generational differences. Through comparative analysis utilizing two C++ inference engines (ONNX Runtime and RTNeural), we aim to evaluate the computational efficiency and timing performance of these models, considering the varying computational loads and the hardware specifics of each device. Our work contributes insights into the feasibility of implementing neural audio processing in real-time on mobile platforms, highlighting challenges and opportunities for future advancements in this rapidly evolving field.
Download Benchmarking Integrated GPU Acceleration of Real-Time Neural Audio Inference on Snapdragon This paper investigates whether integrated GPUs on Qualcomm Snapdragon SoCs can accelerate streaming inference of neural audio models. Five models spanning three orders of magnitude in parameter count are benchmarked across three inference approaches (best available CPU, QNN CPU, and QNN GPU). Results reveal when GPU acceleration offers meaningful gains, when per-call overhead negates benefits, and how model size and architecture determine GPU suitability.