Paper Archive

A clipping prevention method is proposed for first- and second-order all-pass filters with time-varying coefficients. Unlike conventional anti-clipping or declipping approaches, the method operates directly on the coefficient dynamics and does not rely on assumptions about internal energy evolution, by just asking that the input signal is not already clipping. The core idea is to control the deviation between the output of the time-varying filter and that of an equivalent static all-pass structure with constant coefficients. By adaptively limiting this deviation at runtime, the output is constrained below a prescribed clipping threshold (typically unit magnitude). The method is active only during short transients where clipping would occur, after which the coefficients are released to reach their target values. This preserves the integrity of the input signal and the numerical properties of the all-pass filter. Experimental results confirm the expected behavior even in scenarios where energy-preserving all-pass structures exceed the clipping threshold, suggesting the proposed approach as a practical solution for robust dynamic filter implementations with limited additional computational cost, suitable especially for embedded digital audio processing hardware.

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Adaptive room equalization remains challenging under time-varying acoustic conditions and complex excitation signals, such as music. In these scenarios, classical filtered-x least mean squares (Fx-LMS) methods falter due to their rigid formulation. We present a modular differentiable digital signal processing (DDSP) framework for closed-loop adaptive room equalization that recovers Fx-LMS as a special case through automatic differentiation. The framework supports interchangeable EQ structures, response estimation methods, loss functions, and optimizers. Experiments with time-varying measured room impulse responses show that frequency-domain objectives provide more stable adaptation than time-domain objectives in the considered scenarios. Relative to the non-equalized response, system distance is reduced by 70% and mel-spectral distance by 13% (worst-case scenario). We further examine how online room response estimation accuracy and frame length affect the trade-off between responsiveness and convergence stability. Overall, the framework provides a unified open-source basis for exploring synergies between classical adaptive filtering and DDSP-based optimization.

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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.

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Reducing the aliasing of nonlinear functions is an important problem in digital signal processing. The introduction of the Antiderivative Antialiasing (ADAA) method brought many benefits and is an active area of research. The current bottleneck in terms of aliasing reduction is the initial conversion from discrete- to continuous-time, which was previously done by linear interpolation. In this paper we derive PolyADAA, a method for computing the ADAA output when this conversion is done using higher order Lagrange interpolation. To obtain a viable solution, the nonlinear function is approximated using Chebyshev polynomials, which enable the ADAA integral to be computed. The paper provides numerical examples to show the effectiveness of the approach and discusses the advantages of the method.

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Oscillator synchronization is a widely used sound-synthesis technique, but straightforward digital implementations suffer from aliasing artifacts. This paper presents an alias-free method for digital emulation of oscillator synchronization of arbitrary periodic waveforms based on additive synthesis. Starting from a finite set of Fourier-series coefficients representing a bandlimited free-running waveform, we derive linear spectral-resampling transforms that map these coefficients to those of the bandlimited synchronized waveform. Beyond conventional hard synchronization, the proposed approach also supports two additional soft-synchronization modes. To address the high computational complexity of the proposed method, we introduce HASY, a 6 mm² application-specific integrated circuit (ASIC) fabricated in 65 nm CMOS technology. HASY generates one 96 kHz, 24 bit alias-free synchronized waveform with up to 512 harmonics and computes the spectral-resampling transform within only five audio-sample periods.

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Recent advances in AI-assisted coding tools raise questions about how programming-intensive subjects such as audio digital signal processing should be taught and how exam projects should be evaluated. This paper presents a small-scale controlled exploratory study conducted in a graduate course on music DSP. As a final project at the end of the course, the students implemented a modular synthesizer plugin in C++. Half of the students had access to AI-assisted coding support, while the other group developed the plugin manually. All students had to follow a protocol and provide data at the end of the project, together with their code, which was discussed with them as part of the course exam. Although the scale of the study is small, the paper shares a qualitative analysis of the results and a few takeaway messages for future reference among lecturers in the field. Overall, AI-assisted coding does provide some advantage to students but only in certain regards. The used AI tools, trained on GitHub repositories, seem to have only partial awareness of the state of the art in digital audio processing (e.g. antialiasing oscillators, virtual analog filters, etc.). Finally, the use of AI seems to not interfere excessively with the ability of the students to learn from their practical experience.

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This paper presents SCAPES, a semantically conditioned autoregressive prior for environmental sound generation. The system models discrete audio representations using an autoregressive architecture conditioned on semantic information, enabling the generation of environmental sounds that follow user-specified concepts. By learning a prior over audio tokens, SCAPES combines high-level semantic control with detailed temporal modeling. Experimental evaluation investigates the quality, diversity, and semantic consistency of generated sounds, demonstrating the potential of autoregressive priors for controllable environmental sound synthesis.

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We present WildFX, a digital-audio-workstation-powered pipeline for modeling audio-effects graphs from in-the-wild audio. The system uses a DAW environment to construct, render, and evaluate effect-processing graphs, enabling research on realistic effect chains beyond isolated processors or synthetic training settings. WildFX supports the analysis and reconstruction of complex audio transformations by combining flexible plugin routing with data-driven modeling. The pipeline is designed to facilitate scalable dataset creation and experimentation with effect graph inference, parameter estimation, and audio transformation in practical production contexts.

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We introduce FoleySet, a human-annotated Foley sound dataset designed to support research on sound-event understanding and Foley sound generation. The dataset provides annotations at multiple levels of granularity, capturing both broad event categories and more detailed semantic or production-related attributes. This multi-level structure supports tasks such as classification, retrieval, captioning, and controllable generation. FoleySet is intended to address the limited availability of systematically annotated Foley material and to provide a common resource for evaluating models across different levels of semantic detail.

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This paper presents Quality Audio Prototyping (QAP), a unified prototype system for sound retrieval and procedural generation. The system is designed to support rapid exploration of sound effects through a common interface that combines retrieval from existing audio collections with controllable procedural synthesis. By bringing these two paradigms together, QAP allows users to search for recorded sounds, generate new material, and iteratively refine results within a single workflow. The prototype emphasizes usability, extensibility, and practical sound-design applications, providing a foundation for future work on integrated retrieval and generation systems.

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