Paper Archive

Herein, we demonstrate the use of neural nets towards simulating multiport nonlinearities inside a wave digital filter. We introduce a resolved wave definition which allows us to extract features from a Kirchhoff domain dataset and train our neural networks directly in the wave domain. A hyperparameter search is performed to minimize error and runtime complexity. To illustrate the method, we model a tube amplifier circuit inspired by the preamplifier stage of the Fender Pro-Junior guitar amplifier. We analyze the performance of our neural nets models by comparing their distortion characteristics and transconductances. Our results suggest that activation function selection has a significant effect on the distortion characteristic created by the neural net.

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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.

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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.

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Voice conversion has gained increasing popularity within the field of audio manipulation and speech synthesis. Often, the main objective is to transfer the input identity to that of a target speaker without changing its linguistic content. While current work provides high-fidelity solutions they rarely focus on model simplicity, high-sampling rate environments or stream-ability. By incorporating speech representation learning into a generative timbre transfer model, traditionally created for musical purposes, we investigate the realm of voice conversion generated directly in the time domain at high sampling rates. More specifically, we guide the latent space of a baseline model towards linguistically relevant representations and condition it on external speaker information. Through objective and subjective assessments, we demonstrate that the proposed solution can attain levels of naturalness, quality, and intelligibility comparable to those of a state-of-the-art solution for seen speakers, while significantly decreasing inference time. However, despite the presence of target speaker characteristics in the converted output, the actual similarity to unseen speakers remains a challenge.

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In this paper we present the first steps towards the creation of a tool which enables artists to create music visualizations using pretrained, generative, machine learning models. First, we investigate the application of network bending, the process of applying transforms within the layers of a generative network, to image generation diffusion models by utilizing a range of point-wise, tensorwise, and morphological operators. We identify a number of visual effects that result from various operators, including some that are not easily recreated with standard image editing tools. We find that this process allows for continuous, fine-grain control of image generation which can be helpful for creative applications. Next, we generate music-reactive videos using Stable Diffusion by passing audio features as parameters to network bending operators. Finally, we comment on certain transforms which radically shift the image and the possibilities of learning more about the latent space of Stable Diffusion based on these transforms.

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Neural networks have been applied within the Wave Digital Filter (WDF) framework as data-driven models for nonlinear multi-port circuit elements. Conventionally, these models are trained on wave variables obtained by sampling the current-voltage characteristic of the considered nonlinear element before being incorporated into the circuit WDF implementation. However, isolating multi-port elements for this process can be challenging, as their nonlinear behavior often depends on dynamic effects that emerge from interactions with the surrounding circuit. In this paper, we propose a novel approach for training neural models of nonlinear multi-port elements directly within a circuit’s Wave Digital (WD) discretetime implementation, relying solely on circuit input-output voltage measurements. Exploiting the differentiability of WD simulations, we embed the neural network into the simulation process and optimize its parameters using gradient-based methods by minimizing a loss function defined over the circuit output voltage. Experimental results demonstrate the effectiveness of the proposed approach in accurately capturing the nonlinear circuit behavior, while preserving the interpretability and modularity of WDFs.

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Recent studies on classifying electric guitar effects have achieved high accuracy, particularly with deep learning techniques. However, these studies often rely on simplified datasets consisting mainly of single notes rather than realistic guitar recordings. Moreover, in the specific field of effect chain estimation, the literature tends to rely on large models, making them impractical for real-time or resource-constrained applications. In this work, we recorded realistic guitar performances using four different guitars and created three datasets by applying a chain of five effects with increasing complexity: (1) fixed order and parameters, (2) fixed order with randomly sampled parameters, and (3) random order and parameters. We also propose a novel Neural Architecture Search method aimed at discovering accurate yet compact convolutional neural network models to reduce power and memory consumption. We compared its performance to a basic random search strategy, showing that our custom Neural Architecture Search outperformed random search in identifying models that balance accuracy and complexity. We found that the number of convolutional and pooling layers becomes increasingly important as dataset complexity grows, while dense layers have less impact. Additionally, among the effects, tremolo was identified as the most challenging to classify.

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This work focuses on developing an artistic tool that performs an unsupervised mapping between text and sound, converting an input text string into a series of sounds from a given sound corpus. With the use of a pre-trained sound embedding model and a separate, pre-trained text embedding model, the goal is to find a mapping between the two feature spaces. Our approach is unsupervised which allows any sound corpus to be used with the system. The tool performs the task of text-to-sound retrieval, creating a soundfile in which each word in the text input is mapped to a single sound in the corpus, and the resulting sounds are concatenated to play sequentially. We experiment with three different mapping methods, and perform quantitative and qualitative evaluations on the outputs. Our results demonstrate the potential of unsupervised methods for creative applications in text-to-sound mapping.

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The purpose of this presentation is to demonstrate the practical interest of an original improvement of the classic Fourier analysis. The n-th order short-time Fourier Transform (FTn) extends the classic short-time Fourier transform by also considering the first n signal derivatives. This technique greatly improves Fourier analysis precision not only in frequency and amplitude but also in time, thus minimizing the well-known problem of the trade-off of time versus frequency. The implementation of this analysis method leads to an enhanced phase vocoder particularly wellsuited for extracting spectral parameters from the sounds.

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In this transformation we present a rhythmically constrained audio style transfer technique for automatic mixing and mashing of two audio inputs. In this transformation the rhythmic and timbral features of both input signals are combined together through the use of an audio style transfer process that transforms the files so that they adhere to a larger metrical structure of the chosen input. This is accomplished by finding beat boundaries of both inputs and performing the transformation on beat-length audio segments. In order for the system to perform a mashup between two signals, we reformulate the previously used audio style transfer loss terms into three loss functions and enable them to be independent of the input. We measure and compare rhythmic similarities of the transformed and input audio signals using their rhythmic envelopes to investigate the influence of the tested transformation objectives.

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