Download Perceptual Decorrelator Based on Resonators 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.
Download Zero-Phase Sound via Giant FFT 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.
Download Unsupervised Estimation of Nonlinear Audio Effects: Comparing Diffusion-Based and Adversarial Approaches Accurately estimating nonlinear audio effects without access to
paired input-output signals remains a challenging problem. This
work studies unsupervised probabilistic approaches for solving this
task. We introduce a method, novel for this application, based
on diffusion generative models for blind system identification, enabling the estimation of unknown nonlinear effects using blackand gray-box models. This study compares this method with a
previously proposed adversarial approach, analyzing the performance of both methods under different parameterizations of the
effect operator and varying lengths of available effected recordings. Through experiments on guitar distortion effects, we show
that the diffusion-based approach provides more stable results and
is less sensitive to data availability, while the adversarial approach
is superior at estimating more pronounced distortion effects. Our
findings contribute to the robust unsupervised blind estimation of
audio effects, demonstrating the potential of diffusion models for
system identification in music technology.