Download Guitar Tone Stack Modeling with a Neural State-Space Filter In this work, we present a data-driven approach to modeling tone stack circuits in guitar amplifiers and distortion pedals. To this aim, the proposed modeling approach uses a feedforward fully connected neural network to predict the parameters of a coupledform state-space filter, ensuring the numerical stability of the resulting time-varying system. The neural network is conditioned on the tone controls of the target tone stack and is optimized jointly with the coupled-form state-space filter to match the target frequency response. To assess the proposed approach, we model three popular tone stack schematics with both matched-order and overparameterized filters and conduct an objective comparison with well-established approaches that use cascaded biquad filters. Results from the conducted experiments demonstrate improved accuracy of the proposed modeling approach, especially in the case of over-parameterized state-space filters while guaranteeing numerical stability. Our method can be deployed, after training, in realtime audio processors.
Download Automated Calibration of a Parametric Spring Reverb Model The calibration of a digital spring reverberator model is crucial for the authenticity and quality of the sound produced by the model. In this paper, an automated calibration of the model parameters is proposed, by analysing the spectrogram, the energy decay curve, the spectrum, and the autocorrelation of the time signal and spectrogram. A visual inspection of the spectrograms as well as a comparison of sound samples proves the approach to be successful for estimating the parameters of reverberators with one, two and three springs. This indicates that the proposed method is a viable alternative to manual calibration of spring reverberator models.
Download Binaural Dark-Velvet-Noise Reverberator Binaural late-reverberation modeling necessitates the synthesis of frequency-dependent inter-aural coherence, a crucial aspect of spatial auditory perception. Prior studies have explored methodologies such as filtering and cross-mixing two incoherent late reverberation impulse responses to emulate the coherence observed in measured binaural late reverberation. In this study, we introduce two variants of the binaural dark-velvet-noise reverberator. The first one uses cross-mixing of two incoherent dark-velvet-noise sequences that can be generated efficiently. The second variant is a novel time-domain jitter-based approach. The methods’ accuracies are assessed through objective and subjective evaluations, revealing that both methods yield comparable performance and clear improvements over using incoherent sequences. Moreover, the advantages of the jitter-based approach over cross-mixing are highlighted by introducing a parametric width control, based on the jitter-distribution width, into the binaural dark velvet noise reverberator. The jitter-based approach can also introduce timedependent coherence modifications without additional computational cost.
Download One-to-Many Conversion for Percussive Samples A filtering algorithm for generating subtle random variations in
sampled sounds is proposed. Using only one recording for impact
sound effects or drum machine sounds results in unrealistic repetitiveness during consecutive playback. This paper studies spectral
variations in repeated knocking sounds and in three drum sounds:
a hihat, a snare, and a tomtom. The proposed method uses a short
pseudo-random velvet-noise filter and a low-shelf filter to produce
timbral variations targeted at appropriate spectral regions, yielding potentially an endless number of new realistic versions of a
single percussive sampled sound.
The realism of the resulting
processed sounds is studied in a listening test. The results show
that the sound quality obtained with the proposed algorithm is at
least as good as that of a previous method while using 77% fewer
computational operations. The algorithm is widely applicable to
computer-generated music and game audio.
Download Real-Time Black-Box Modelling With Recurrent Neural Networks This paper proposes to use a recurrent neural network for black-box modelling of nonlinear audio systems, such as tube amplifiers and distortion pedals. As a recurrent unit structure, we test both Long Short-Term Memory and a Gated Recurrent Unit. We compare the proposed neural network with a WaveNet-style deep neural network, which has been suggested previously for tube amplifier modelling. The neural networks are trained with several minutes of guitar and bass recordings, which have been passed through the devices to be modelled. A real-time audio plugin implementing the proposed networks has been developed in the JUCE framework. It is shown that the recurrent neural networks achieve similar accuracy to the WaveNet model, while requiring significantly less processing power to run. The Long Short-Term Memory recurrent unit is also found to outperform the Gated Recurrent Unit overall. The proposed neural network is an important step forward in computationally efficient yet accurate emulation of tube amplifiers and distortion pedals.
Download Sample Rate Independent Recurrent Neural Networks for Audio Effects Processing In recent years, machine learning approaches to modelling guitar amplifiers and effects pedals have been widely investigated and have become standard practice in some consumer products. In particular, recurrent neural networks (RNNs) are a popular choice for modelling non-linear devices such as vacuum tube amplifiers and distortion circuitry. One limitation of such models is that they are trained on audio at a specific sample rate and therefore give unreliable results when operating at another rate. Here, we investigate several methods of modifying RNN structures to make them approximately sample rate independent, with a focus on oversampling. In the case of integer oversampling, we demonstrate that a previously proposed delay-based approach provides high fidelity sample rate conversion whilst additionally reducing aliasing. For non-integer sample rate adjustment, we propose two novel methods and show that one of these, based on cubic Lagrange interpolation of a delay-line, provides a significant improvement over existing methods. To our knowledge, this work provides the first in-depth study into this problem.
Download Realistic Gramophone Noise Synthesis Using a Diffusion Model This paper introduces a novel data-driven strategy for synthesizing gramophone noise audio textures. A diffusion probabilistic model is applied to generate highly realistic quasiperiodic noises. The proposed model is designed to generate samples of length equal to one disk revolution, but a method to generate plausible periodic variations between revolutions is also proposed. A guided approach is also applied as a conditioning method, where an audio signal generated with manually-tuned signal processing is refined via reverse diffusion to improve realism. The method has been evaluated in a subjective listening test, in which the participants were often unable to recognize the synthesized signals from the real ones. The synthetic noises produced with the best proposed unconditional method are statistically indistinguishable from real noise recordings. This work shows the potential of diffusion models for highly realistic audio synthesis tasks.
Download Musical Signal Analysis Using Fractional-Delay Inverse Comb Filters A novel filter configuration for the analysis of harmonic musical signals is proposed. The method is based on inverse comb filtering that allows for the extraction of selected harmonic components or the background noise component between the harmonic spectral components. A highly accurate delay required in the inverse comb filter is implemented with a high-order allpass filter. The paper shows that the filter is easy to design, efficient to implement, and it enables accurate low-level feature analysis of musical tones. We describe several case studies to demonstrate the effectiveness of the proposed approach: isolating a single partial from a synthetic signal, analyzing the even-to-odd ratio of harmonics in a clarinet tone, and extracting the residual from a bowed string tone.
Download RIR2FDN: An Improved Room Impulse Response Analysis and Synthesis This paper seeks to improve the state-of-the-art in delay-networkbased analysis-synthesis of measured room impulse responses (RIRs). We propose an informed method incorporating improved energy decay estimation and synthesis with an optimized feedback delay network. The performance of the presented method is compared against an end-to-end deep-learning approach. A formal listening test was conducted where participants assessed the similarity of reverberated material across seven distinct RIRs and three different sound sources. The results reveal that the performance of these methods is influenced by both the excitation sounds and the reverberation conditions. Nonetheless, the proposed method consistently demonstrates higher similarity ratings compared to the end-to-end approach across most conditions. However, achieving an indistinguishable synthesis of measured RIRs remains a persistent challenge, underscoring the complexity of this problem. Overall, this work helps improve the sound quality of analysis-based artificial reverberation.
Download A Diffusion-Based Generative Equalizer for Music Restoration This paper presents a novel approach to audio restoration, focusing on the enhancement of low-quality music recordings, and in particular historical ones. Building upon a previous algorithm called BABE, or Blind Audio Bandwidth Extension, we introduce BABE-2, which presents a series of improvements. This research broadens the concept of bandwidth extension to generative equalization, a task that, to the best of our knowledge, has not been previously addressed for music restoration. BABE-2 is built around an optimization algorithm utilizing priors from diffusion models, which are trained or fine-tuned using a curated set of high-quality music tracks. The algorithm simultaneously performs two critical tasks: estimation of the filter degradation magnitude response and hallucination of the restored audio. The proposed method is objectively evaluated on historical piano recordings, showing an enhancement over the prior version. The method yields similarly impressive results in rejuvenating the works of renowned vocalists Enrico Caruso and Nellie Melba. This research represents an advancement in the practical restoration of historical music. Historical music restoration examples are available at: research.spa.aalto.fi/publications/papers/dafx-babe2/.