Paper Archive

Discrete-time modeling of acoustic, mechanical and electrical systems is a prominent topic in the musical signal processing literature. Such models are mostly derived by discretizing a mathematical model, given in terms of ordinary or partial differential equations, using established techniques. Recent work has applied the techniques of machine-learning to construct such models automatically from data for the case of systems which have lumped states described by scalar values, such as electrical circuits. In this work, we examine how similar techniques are able to construct models of systems which have spatially distributed rather than lumped states. We describe several novel recurrent neural network structures, and show how they can be thought of as an extension of modal techniques. As a proof of concept, we generate synthetic data for three physical systems and show that the proposed network structures can be trained with this data to reproduce the behavior of these systems.

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Decorrelation filters transform mono audio into multiple decorrelated copies. This paper introduces a novel decorrelation filter design based on a resonator bank, which produces a sum of over a thousand exponentially decaying sinusoids. A headphone listening test was used to identify the minimum inter-channel time delays that perceptually match ERB-filtered coherent noise to corresponding incoherent noise. The decay rate of each resonator is set based on a group delay profile determined by the listening test results at its corresponding frequency. Furthermore, the delays from the test are used to refine frequency-dependent windowing in coherence estimation, which we argue represents the perceptually most accurate way of assessing interaural coherence. This coherence measure then guides an optimization process that adjusts the initial phases of the sinusoids to minimize the coherence between two instances of the resonator-based decorrelator. The delay results establish the necessary group delay per ERB for effective decorrelation, revealing higher-than-expected values, particularly at higher frequencies. For comparison, the optimization is also performed using two previously proposed group-delay profiles: one based on the period of the ERB band center frequency and another based on the maximum group-delay limit before introducing smearing. The results indicate that the perceptually informed profile achieves equal decorrelation to the latter profile while smearing less at high frequencies. Overall, optimizing the phase response of the proposed decorrelator yields significantly lower coherence compared to using a random phase.

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Given the speedy computation of the FFT in current computer hardware, there are new possibilities for examining transformations for very long sounds. A zero-phase version of any audio signal can be obtained by zeroing the phase angle of its complex spectrum and taking the inverse FFT. This paper recommends additional processing steps, including zero-padding, transient suppression at the signal’s start and end, and gain compensation, to enhance the resulting sound quality. As a result, a sound with the same spectral characteristics as the original one, but with different temporal events, is obtained. Repeating rhythm patterns are retained, however. Zero-phase sounds are palindromic in the sense that they are symmetric in time. A comparison of the zero-phase conversion to the autocorrelation function helps to understand its properties, such as why the rhythm of the original sound is emphasized. It is also argued that the zero-phase signal has the same autocorrelation function as the original sound. One exciting variation of the method is to apply the method separately to the real and imaginary parts of the spectrum to produce a stereo effect. A frame-based technique enables the use of the zero-phase conversion in real-time audio processing. The zero-phase conversion is another member of the giant FFT toolset, allowing the modification of sampled sounds, such as drum loops or entire songs.

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This paper proposes dark velvet noise (DVN) as an extension of the original velvet noise with a lowpass spectrum. The lowpass spectrum is achieved by allowing each pulse in the sparse sequence to have a randomized pulse width. The cutoff frequency is controlled by the density of the sequence. The modulated pulse-width can be implemented efficiently utilizing a discrete set of recursive running-sum filters, one for each unique pulse width. DVN may be used in reverberation algorithms. Typical room reverberation has a frequency-dependent decay, where the high frequencies decay faster than the low ones. A similar effect is achieved by lowering the density and increasing the pulse-width of DVN in time, thereby making the DVN suitable for artificial reverberation.

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Artificial reverberation algorithms often suffer from spectral coloration, usually in the form of metallic ringing, which impairs the perceived quality of sound. This paper proposes a method to reduce the coloration in the feedback delay network (FDN), a popular artificial reverberation algorithm. An optimization framework is employed entailing a differentiable FDN to learn a set of parameters decreasing coloration. The optimization objective is to minimize the spectral loss to obtain a flat magnitude response, with an additional temporal loss term to control the sparseness of the impulse response. The objective evaluation of the method shows a favorable narrower distribution of modal excitation while retaining the impulse response density. The subjective evaluation demonstrates that the proposed method lowers perceptual coloration of late reverberation, and also shows that the suggested optimization improves sound quality for small FDN sizes. The method proposed in this work constitutes an improvement in the design of accurate and high-quality artificial reverberation, simultaneously offering computational savings.

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The common-slope model is used to model late reverberation of complex room geometries such as multiple coupled rooms. The model fits band-limited room impulse responses using a set of common decay rates, with amplitudes varying based on listener positions. This paper investigates amplitude estimation methods within the common-slope model framework. We compare several traditional least squares estimation methods and propose using LINEX regression, a Maximum Likelihood approach using logsquared RIR statistics. Through statistical analysis and simulation tests, we demonstrate that LINEX regression improves accuracy and reduces bias when compared to traditional methods.

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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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