Download Removing Crackle from an LP Record via Wavelet Analysis The familiar “crackling” is one of the undesirable phenomena which we deal with in an LP record. Wavelet analysis brings a new alternative approach to the removal of this feature in the restoration process of the recording. In the paper, the principle of this method is described. A theoretical discussion of how the selection of the wavelet basis affects the quality of the restoration is also included.
Download Real-time Audio Processing via Segmented Wavelet Transform In audio applications it is often necessary to process the signal in “real time”. The method of segmented wavelet transform (SegWT) makes it possible to compute the discrete-time wavelet transform of a signal segment-by-segment, not using the classical “windowing”. This means that the method could be utilized for wavelettype processing of an audio signal in real time, or alternatively in case we just need to process a long signal, but there is insufficient computational memory capacity for it (e.g. in the DSPs). In the paper, the principle of the segmented forward wavelet transform is explained and the algorithm is described in detail.
Download VST Plug-in Module Performing Wavelet Transform in Real-time The paper presents a variant of the segmentwise wavelet transform (blockwise DWT, online DWT or SegDWT) algorithm adapted to real-time audio processing. The implementation of the algorithm as a VST plugin is presented as well. The main problem of segmentwise wavelet coefficient processing is the handling of the segment borders. The common border extension methods result in “false” coefficients, which in turn result in border distortion (block-end effects) after particular types of coefficient processing. In contrast, the SegDWT algorithm employs a segment extension technique to prevent this inconvenience and produce exactly the same coefficients as the wavelet transform of the whole signal would do. In this paper we remove some of the shortcomings of the original SegDWT algorithm; for example the need for the “right” segment extension is canceled. The VST plugin module created is described from the viewpoints of both the user and the programmer; the latter can easily add their own method for processing the coefficients.
Download Flexible Framework for Audio Restoration The paper presents a unified, flexible framework for the tasks
of audio inpainting, declipping, and dequantization. The concept is
further extended to cover analogous degradation models in a transformed domain, e.g. quantization of the signal’s time-frequency
coefficients. The task of reconstructing an audio signal from degraded observations in two different domains is formulated as an
inverse problem, and several algorithmic solutions are developed.
The viability of the presented concept is demonstrated on an example where audio reconstruction from partial and quantized observations of both the time-domain signal and its time-frequency
coefficients is carried out.
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.