gpuRIR: A python library for Room Impulse Response simulation with GPU acceleration
원문 초록
The Image Source Method (ISM) is one of the most employed techniques to calculate acoustic Room Impulse Responses (RIRs), however, its computational complexity grows fast with the reverberation time of the room and its computation time can be prohibitive for some applications where a huge number of RIRs are needed. In this paper, we present a new implementation that dramatically improves the computation speed of the ISM by using Graphic Processing Units (GPUs) to parallelize both the simulation of multiple RIRs and the computation of the images inside each RIR. We provide a Python library under GNU license that can be easily used without any knowledge about GPU programming and we show that it is about 100 times faster than other state of the art CPU libraries.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
MathOptAI.jl: Embed trained machine learning predictors into JuMP models
ConsumerBench: Benchmarking Generative AI Applications on End-User Devices
Versatile and Fast Location-Based Private Information Retrieval with Fully Homomorphic Encryption over the Torus
SecONNds: Secure Outsourced Neural Network Inference on ImageNet
Plug-and-Play Linear Attention for Pre-trained Image and Video Restoration Models
FlashDMoE: Fast Distributed MoE in a Single Kernel
토론
이 논문과 연결된 포럼 글이 아직 없습니다.