Paper Archive

Scattering delay networks (SDNs) provide a flexible and efficient framework for artificial reverberation and room acoustic modeling. In this work, we introduce a differentiable SDN, enabling gradient-based optimization of its parameters to better approximate the acoustics of real-world environments. By formulating key parameters such as scattering matrices and absorption filters as differentiable functions, we employ gradient descent to optimize an SDN based on a target room impulse response. Our approach minimizes discrepancies in perceptually relevant acoustic features, such as energy decay and frequency-dependent reverberation times. Experimental results demonstrate that the learned SDN configurations significantly improve the accuracy of synthetic reverberation, highlighting the potential of data-driven room acoustic modeling.

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Reverberation is crucial in the acoustical design of physical spaces, especially halls for live music performances. Reverberation Enhancement Systems (RESs) are active acoustic systems that can control the reverberation properties of physical spaces, allowing them to adapt to specific acoustical needs. The performance of RESs strongly depends on the properties of the physical room and the architecture of the Digital Signal Processor (DSP). However, room-impulse-response (RIR) measurements and the DSP code from previous studies on RESs have never been made open access, leading to non-reproducible results. In this study, we present DataRES and PyRES—a RIR dataset and a Python library to increase the reproducibility of studies on RESs. The dataset contains RIRs measured in RES research and development rooms and professional music venues. The library offers classes and functionality for the development, evaluation, and simulation of RESs. The implemented DSP architectures are made differentiable, allowing their components to be trained in a machine-learning-like pipeline. The replication of previous studies by the authors shows that PyRES can become a useful tool in future research on RESs.

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Spatial Room Impulse Responses (SRIRs) are fundamental to immersive audio rendering and have become a key focus of recent machine learning research in acoustics and auralization. Due to the high computational cost of direct convolution, spatial audio systems commonly employ artificial reverberation algorithms. However, these approaches often fail to accurately reproduce the spatial, temporal, and spectral characteristics of early reflections, leading to notable deviations from measured SRIRs. This paper presents a comprehensive framework for the analysis and efficient resynthesis of SRIRs captured with Spherical Microphone Arrays (SMAs). The proposed method accounts for hardware-induced artifacts, including scattering and spatial aliasing. Early reflections are reconstructed using a parametric approach based on the Herglotz analysis method, while late reverberation is synthesized using a Directional Feedback Delay Network (DFDN) with optimized filter-attenuation and correlation-matching. The proposed framework produces signals whose spatial correlation and Energy Decay Relief (EDR) closely match those of measured SRIRs, demonstrating its effectiveness for both real-time spatial audio rendering and realistic dataset generation for machine learning applications.

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This paper deals with the automatic and robust analysis, and the realistic and low-cost synthesis of percussive drilling like sounds. The two contributions are: a non-supervised removal of quasistationary background noise based on the Non-negative Matrix Factorization, and a granular method for analysis/synthesis of this drilling sounds. These two points are appropriate to the acoustical properties of percussive drilling sounds, and can be extended to other sounds with similar characteristics. The context of this work is the training of operators of working machines using simulators. Additionally, an implementation is explained.

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Serious games can be used for training orientation and mobility skills of visually impaired children and youngsters. Here we present a serious game for training sound localization skills and concepts usually covered at orientation and mobility classes, such as front/back and left/right. In addition, the game helps the players to train simple body rotation mobility skills. The game was designed for touch screen mobile devices and has an audio virtual environment created with 3D spatialized audio obtained with head-related transfer functions. The results from a usability test with blind students show that the game can have a positive impact on the players’ skills, namely on their motor coordination and localization skills, as well as on their self-confidence.

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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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Information about the human spoken and singing voice is conveyed through the articulations of the individual’s vocal folds and vocal tract. The signal receiver, either human or machine, works at different levels of abstraction to extract and interpret only the relevant context specific information needed. Traditionally in the field of human machine interaction, the human voice is used to drive and control events that are discrete in terms of time and value. We propose to use the voice as a source of realvalued and time-continuous control signals that can be employed to interact with any multidimensional human-controllable device in real-time. The isolation of noise sources and the independence of the control dimensions play a central role. Their dependency on individual voice represents an additional challenge. In this paper we introduce a method to compute case specific independent signals from the vocal sound, together with an individual study of features computation and selection for noise rejection.

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Similarity is a key concept for estimating associations among a set of objects. Music similarity is usually exploited to retrieve relevant items from a dataset containing audio tracks. In this work, we approach the problem of semantic similarity between short pieces of music by analysing their instrumentations. Our aim is to label audio excerpts with the most salient instruments (e.g. piano, human voice, drums) and use this information to estimate a semantic relation (i.e. similarity) between them. We present 3 different methods for integrating along an audio excerpt frame-based classifier decisions to derive its instrumental content. Similarity between audio files is then determined solely by their attached labels. We evaluate our algorithm in terms of label assignment and similarity assessment, observing significant differences when comparing it to commonly used audio similarity metrics. In doing so we test on music from various genres of Western music to simulate real world scenarios.

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The interaural thresholded level distribution (ITLD) is a novel metric of auditory source width (ASW), derived from the psychophysical processes and structures of the inner ear. While several of the ITLD’s objective properties have been presented in previous work, its frequency-dependent characteristics and perceptual relationship with ASW have not been previously explored. This paper presents an investigation into these properties of the ITLD, which exhibits pronounced variation in band-limited behaviour as octaveband centre-frequency is increased. Additionally, a very strong correlation was found between [1 – ITLD] and normalised values of ASW, collected from a semantic differential listening test based on the Multiple Stimulus with Hidden Reference and Anchor (MUSHRA) framework. Perceptual relationships between various ITLD-derived quantities were also investigated, showing that the low-pass filter intrinsic to ITLD calculation strengthened the relationship between [1 – ITLD] and ASW. A subsequent test using transient stimuli, as well as investigations into other psychoacoustic properties of the metric such as its just-noticeabledifference, were outlined as subjects for future research, to gain a deeper understanding of the subjective properties of the ITLD.

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Most real systems, from acoustics to analog electronics, are characterised by bidirectional coupling amongst elements rather than neat, unidirectional signal flows between self-contained modules. Integrating digital processing into physical domains becomes a significant engineering challenge when the application requires bidirectional coupling across the physical-digital boundary rather than separate, well-defined inputs and outputs. We introduce an approach to hybrid analog-digital audio processing using synthetic impedance: digitally simulated circuit elements integrated into an otherwise analog circuit. This approach combines the physicality and classic character of analog audio circuits alongside the precision and flexibility of digital signal processing (DSP). Our impedance synthesis system consists of a voltage-controlled current source and a microcontroller-based DSP system. We demonstrate our technique through modifying an iconic guitar distortion pedal, the Boss DS-1, showing the ability of the synthetic impedance to both replicate and extend the behaviour of the pedal’s diode clipping stage. We discuss the behaviour of the synthetic impedance in isolated laboratory conditions and in the DS-1 pedal, highlighting the technical and creative potential of the technique as well as its practical limitations and future extensions.

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