Download Unsupervised Estimation of Nonlinear Audio Effects: Comparing Diffusion-Based and Adversarial Approaches Accurately estimating nonlinear audio effects without access to
paired input-output signals remains a challenging problem. This
work studies unsupervised probabilistic approaches for solving this
task. We introduce a method, novel for this application, based
on diffusion generative models for blind system identification, enabling the estimation of unknown nonlinear effects using blackand gray-box models. This study compares this method with a
previously proposed adversarial approach, analyzing the performance of both methods under different parameterizations of the
effect operator and varying lengths of available effected recordings. Through experiments on guitar distortion effects, we show
that the diffusion-based approach provides more stable results and
is less sensitive to data availability, while the adversarial approach
is superior at estimating more pronounced distortion effects. Our
findings contribute to the robust unsupervised blind estimation of
audio effects, demonstrating the potential of diffusion models for
system identification in music technology.
Download Exploring Parallelism and Energy Efficiency in a Multistage Linear-Phase Octave Filter Bank This paper presents a high-performance and energy-aware implementation of a multistage linear-phase octave filter bank for edge system-on-chip (SoC) platforms. The algorithm relies on a cascade of stretched FIR filter stages and complementary band splitting to preserve linear phase across all outputs. While effective, mapping such structures to embedded multicore CPUs introduces significant challenges regarding state management, task synchronization, memory-traffic efficiency, and energy-aware execution. These issues are especially relevant in block-based edge-audio processing, where high throughput must be balanced against the power constraints of mobile and embedded devices. We derive a cache-friendly sequential realization using a blocked streaming schedule and compact circular state. Building on this, we propose a parallel design based on an OpenMP task pipeline with explicit dependencies to preserve the filter-bank semantics without fine-grained synchronization in the filtering tasks. Experimental results on an NVIDIA Jetson Orin Nano module show that the optimized sequential version already sustains more than 1.18 M samples/s, while the task-level pipeline reaches speedups above 4.5× for suitable block sizes. Furthermore, our analysis reveals a clear trade-off between throughput and power, showing that the most energy-efficient operating point does not necessarily coincide with maximum performance on multicore edge SoCs.
Download Shimmer Reverberation with Nonlinear Feedback Delay Networks Shimmer reverberation is an effect used in music production to deliver ethereal, pitch-shifted textures and evolving ambient soundscapes. This paper explores the synthesis of shimmer effects using the feedback delay network architecture, a popular real-time reverberator. We propose five distinct approaches for integrating nonlinear and time-varying operations into the feedback loop, focusing on expanding the harmonic content while adhering to energy-preservation and stability criteria. Our approach can generate a wide range of sonic characteristics, from harmonically rich distortions to musically coherent pitch-shifted reverberation, while maintaining stability and controllable decay behavior.