Download Band-Limited Impulse Invariance Method Using Lagrange Kernels The band-limited impulse invariance method is a recently proposed approach for the discrete-time modeling of an LTI continuoustime system. Both the magnitude and phase responses are accurately modeled by means of discrete-time filters. It is an extension of the conventional impulse invariance method, which is based on the time-domain sampling of the continuous-time response. The resulting IIR filter typically exhibits spectral aliasing artifacts. In the band-limited impulse invariance method, an FIR filter is combined in parallel with the IIR filter, in such a way that the frequency response of the FIR part reduces the aliasing contributions. This method was shown to improve the frequency-domain accuracy while maintaining the compact temporal structure of the discrete-time model. In this paper, a new version of the bandlimited impulse invariance method is introduced, where the FIR coefficients are derived in closed form by examining the discontinuities that occur in the continuous-time domain. An analytical anti-aliasing filtering is performed by replacing the discontinuities with band-limited transients. The band-limited discontinuities are designed by using the anti-derivatives of the Lagrange interpolation kernel. The proposed method is demonstrated by a wave scattering example, where the acoustical impulse responses on a rigid spherical scatter are simulated.
Download GPGPU Audio Benchmark Framework Acceleration of audio workloads on generally-programmable GPU (GPGPU) hardware offers potentially high speedup factors, but also presents challenges in terms of development and deployment. We can increasingly depend on such hardware being available in users’ systems, yet few real-time audio products use this resource. We propose a suite of benchmarks to qualify a GPU as suitable for batch or real-time audio processing. This includes both microbenchmarks and higher-level audio domain benchmarks. We choose metrics based on application, paying particularly close attention to latency tail distribution. We propose an extension to the benchmark framework to more accurately simulate the real-world request pattern and performance requirements when running in a digital audio workstation. We run these benchmarks on two common consumer-level platforms: a PC desktop with a recent midrange discrete GPU and a Macintosh desktop with unified CPUGPU memory architecture.
Download RIR2FDN: An Improved Room Impulse Response Analysis and Synthesis This paper seeks to improve the state-of-the-art in delay-networkbased analysis-synthesis of measured room impulse responses (RIRs). We propose an informed method incorporating improved energy decay estimation and synthesis with an optimized feedback delay network. The performance of the presented method is compared against an end-to-end deep-learning approach. A formal listening test was conducted where participants assessed the similarity of reverberated material across seven distinct RIRs and three different sound sources. The results reveal that the performance of these methods is influenced by both the excitation sounds and the reverberation conditions. Nonetheless, the proposed method consistently demonstrates higher similarity ratings compared to the end-to-end approach across most conditions. However, achieving an indistinguishable synthesis of measured RIRs remains a persistent challenge, underscoring the complexity of this problem. Overall, this work helps improve the sound quality of analysis-based artificial reverberation.