Download Flexible Real-Time Reverberation Synthesis With Accurate Parameter Control Reverberation is one of the most important effects used in audio
production. Although nowadays numerous real-time implementations of artificial reverberation algorithms are available, many of
them depend on a database of recorded or pre-synthesized room
impulse responses, which are convolved with the input signal. Implementations that use an algorithmic approach are more flexible
but do not let the users have full control over the produced sound,
allowing only a few selected parameters to be altered. The realtime implementation of an artificial reverberation synthesizer presented in this study introduces an audio plugin based on a feedback delay network (FDN), which lets the user have full and detailed insight into the produced reverb. It allows for control of
reverberation time in ten octave bands, simultaneously allowing
adjusting the feedback matrix type and delay-line lengths. The
proposed plugin explores various FDN setups, showing that the
lowest useful order for high-quality sound is 16, and that in the
case of a Householder matrix the implementation strongly affects
the resulting reverberation. Experimenting with delay lengths and
distribution demonstrates that choosing too wide or too narrow a
length range is disadvantageous to the synthesized sound quality.
The study also discusses CPU usage for different FDN orders and
plugin states.
Download Differentiable All-Pass Filters for Phase Response Estimation and Automatic Signal Alignment Virtual analog (VA) audio effects are increasingly based on neural networks and deep learning frameworks. Due to the underlying black-box methodology, a successful model will learn to approximate the data it is presented, including potential errors such as latency and audio dropouts as well as non-linear characteristics and frequency-dependent phase shifts produced by the hardware. The latter is of particular interest as the learned phase-response might cause unwanted audible artifacts when the effect is used for creative processing techniques such as dry-wet mixing or parallel compression. To overcome these artifacts we propose differentiable signal processing tools and deep optimization structures for automatically tuning all-pass filters to predict the phase response of different VA simulations, and align processed signals that are out of phase. The approaches are assessed using objective metrics while listening tests evaluate their ability to enhance the quality of parallel path processing techniques. Ultimately, an overparameterized, BiasNet-based, all-pass model is proposed for the optimization problem under consideration, resulting in models that can estimate all-pass filter coefficients to align a dry signal with its affected, wet, equivalent.
Download An active learning procedure for the interaural time difference discrimination threshold Measuring the auditory lateralization elicited by interaural time difference (ITD) cues involves the estimation of a psychometric function (PF). The shape of this function usually follows from the analysis of the subjective data and models the probability of correctly localizing the angular position of a sound source. The present study describes and evaluates a procedure for progressively fitting a PF, using Gaussian process classification of the subjective responses produced during a binary decision experiment. The process refines adaptively an approximated PF, following Bayesian inference. At each trial, it suggests the most informative auditory stimulus for function refinement according to Bayesian active learning by disagreement (BALD) mutual information. In this paper, the procedure was modified to accommodate two-alternative forced choice (2AFC) experimental methods and then was compared with a standard adaptive “three-down, one-up” staircase procedure. Our process approximates the average threshold ITD 79.4% correct level of lateralization with a mean accuracy increase of 8.9% over the Weibull function fitted on the data of the same test. The final accuracy for the Just Noticeable Difference (JND) in ITD is achieved with only 37.6% of the trials needed by a standard lateralization test.