Reverberation is a key element in spatial audio perception, historically achieved with the use of analogue devices, such as plate and spring reverb, and in the last decades with digital signal processing techniques that have allowed different approaches for Virtual Analogue Modelling (VAM). The electromechanical functioning of the spring reverb makes it a nonlinear system that is difficult to fully emulate in the digital domain with white-box modelling techniques. In this study, we compare five different neural network architectures, including convolutional and recurrent models, to assess their effectiveness in replicating the characteristics of this audio effect. The evaluation is conducted on two datasets at sampling rates of 16 kHz and 48 kHz. This paper specifically focuses on neural audio architectures that offer parametric control, aiming to advance the boundaries of current black-box modelling techniques in the domain of spring reverberation.

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This paper presents a headphone-based audio augmented reality demonstrator showcasing the effects of manipulated late reverberation in rendering virtual sound sources. The setup is based on a dataset of binaural room impulse responses measured along a 2 m long line, which is used to imitate the reproduction of a pair of loudspeakers. Therefore, listeners can explore the virtual sources by moving back and forth and rotating arbitrarily on this line. The demo allows the user to adjust the late reverberation tail of the auralizations interactively from shorter to longer decay times regarding the baseline decay behavior. Modification of the decay times is based on resynthesizing the late reverberation using frequencydependent shaping of binaural white noise and modal reconstruction. The paper includes descriptions of the frameworks used for this demo and an overview of the required data and processing steps.

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Predominant Local Pulse (PLP) estimation, an established method for extracting beat positions and other periodic pulse information from audio signals, has recently been extended with an online variant tailored for real-time applications. In this paper, we introduce a novel approach to generating various real-time control signals from the original online PLP output. While the PLP activation function encodes both predominant pulse information and pulse stability, we propose several normalization procedures to discern local pulse oscillation from stability, utilizing the PLP activation envelope. Through this, we generate pulse-synchronous Low Frequency Oscillators (LFOs) and supplementary confidence-based control signals, enabling dynamic control over audio effect parameters in real-time. Additionally, our approach enables beat position prediction, providing a look-ahead capability, for example, to compensate for system latency. To showcase the effectiveness of our control signals, we introduce an audio plugin prototype designed for integration within a Digital Audio Workstation (DAW), facilitating real-time applications of beat-synchronous effects during live mixing and performances. Moreover, this plugin serves as an educational tool, providing insights into PLP principles and the tempo structure of analyzed music signals.

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Frequency-modulated (FM) sinusoids are commonly used to model signals in several engineering applications, such as radar, sonar, communications, acoustics, and optics. The estimation of the parameters of FM sinusoids is a challenging problem with a long history in the literature. In this article, we use the distribution derivative method (DDM) to estimate the parameters of FM sinusoids in additive white Gaussian noise. Firstly, we derive the estimation of parameters of the model with DDM. Then, we compare the results of Monte-Carlo simulations (MCS) of DDM estimation of FM signals in additive white Gaussian noise against the state of the art (SOTA) and the Cramér-Rao lower bound (CRLB). DDM estimation of FM sinusoids showed performance comparable to the SOTA with less estimation bias. Additionally, DDM estimation of FM sinusoids is simple and straightforward to implement with the fast Fourier transform (FFT) relative to other approaches in the literature. Finally, DDM estimation has effectively the same computational complexity as the FFT.

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Topology provides global invariants for data as well as spaces of deformation. In this paper we discuss the deformations of audio signals which preserve topological information specified by sublevel set persistent homology. It is well known that the topological information only changes at extrema. We introduce box snakes as a data structure that captures permissible editing and deformation of signals and preserves the extremal properties of the signal while allowing for monotone deformations between them. The resulting algorithm works on any ordered discrete data hence can be applied to time and frequency domain finite length audio signals.

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Statement and calculation of objective audio haptic transducer performance metrics facilitates optimisation of multi-sensory sound reproduction systems. Measurements of existing haptic transducers are applied to the calculation of a series of performance metrics to demonstrate a means of comparative objective analysis. The frequency response, transient response and moving mass excursion characteristics of each measured transducer are quantified using novel and previously defined metrics. Objective data drawn from a series of practical measurements shows that the proposed metrics and means of excursion modelling applied herein are appropriate for haptic transducer evaluation and protection against over-excursion respectively.

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Infinite impulse response filters are an essential building block of many time-varying audio systems, such as audio effects and synthesisers. However, their recursive structure impedes end-toend training of these systems using automatic differentiation. Although non-recursive filter approximations like frequency sampling and frame-based processing have been proposed and widely used in previous works, they cannot accurately reflect the gradient of the original system. We alleviate this difficulty by reexpressing a time-varying all-pole filter to backpropagate the gradients through itself, so the filter implementation is not bound to the technical limitations of automatic differentiation frameworks. This implementation can be employed within audio systems containing filters with poles for efficient gradient evaluation. We demonstrate its training efficiency and expressive capabilities for modelling real-world dynamic audio systems on a phaser, time-varying subtractive synthesiser, and feed-forward compressor. We make our code and audio samples available and provide the trained audio effect and synth models in a VST plugin1 .

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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.

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Aliasing is an inherent problem in nonlinear digital audio processing which results in undesirable audible artefacts. Antiderivative antialiasing has proved to be an effective approach to mitigate aliasing distortion, and is based on continuous-time convolution of a linearly interpolated distorted signal with antialiasing filter kernels. However, the performance of this method is determined by the properties of interpolation filter. In this work, cubic interpolation kernels for antiderivative antialiasing are considered. For memoryless nonlinearities, aliasing reduction is improved employing cubic interpolation. For stateful systems, numerical simulation and stability analysis with respect to different interpolation kernels remain in favour of linear interpolation.

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The development of audio Digital Signal Processing (DSP) algorithms typically requires iterative design, analysis, and testing, possibly on different target platforms, furthermore often asking for resets or restarts of execution environments between iterations. Manually performing deployment, setup, and output data collection can quickly become intolerably time-consuming. Therefore, we propose a new, experimental, open-ended, and automatable protocol to separate the coding, building, and deployment tasks onto different network nodes. The proposed protocol is mostly based on widespread technology and designed to be easy to implement and integrate with existing software infrastructure. Its flexibility has been validated through a proof-of-concept implementation. Despite being still in its infancy, it already shows potential in allowing faster and more comfortable development workflows.

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