Paper Archive

Extraction of stationary and transient components from audio has many potential applications to audio effects for audio content production. In this paper we explore stationary/transient separation using convolutional autoencoders. We propose two novel unsupervised algorithms for individual and and joint separation. We describe our implementation and show examples. Our results show promise for the use of convolutional autoencoders in the extraction of sparse components from audio spectrograms, particularly using monophonic sounds.

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This demonstration presents Praat AudioTools, an open-source hybrid toolkit that repurposes Praat's phonetic-analysis environment for electroacoustic composition, sound design, and offline analysis–resynthesis workflows. Rather than treating analysis data as temporary measurements hidden inside an audio processor, Praat AudioTools exposes pitch contours, formant structures, temporal segmentations, spectral descriptors, phrase boundaries, stochastic trajectories, and host-application exchange files as editable compositional objects. These objects can be inspected, modified, chained, reused, and rendered into new sound transformations. The demonstration focuses on seven offline workflows: Neural Ambient Drone Designer, Praat for Max and Max for Live, Phase-Space Composer, Reich Generator, MCMC Musical Variation, Messagesquisse Opening, and Vector/Full-Chain composition workflows. None of the examples are presented as real-time effects. Instead, they show an "edit-in-the-middle" model in which sound is analyzed, intermediate representations are made visible, compositional decisions are applied to those representations, and the result is rendered as audio. The aim is to demonstrate a transparent alternative to both conventional black-box audio effects and end-to-end generative audio systems: a compositional environment where analysis objects become controllers, traces, scores, and reproducible technical artifacts.

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The separation of musical instruments acoustically mixed in one source is a very active field which has been approached from many different viewpoints. This article compares the blind source separation perspective and oscillatory correlation theory taking the auditory scene analysis as the point of departure (ASA). The former technique deals with the separation of a particular signal from a mixture with many others from a statistical point of view. Through the standard Independent Component Analysis (ICA), a blind source separation can be done using the particular and the mixed signals' statistical properties. Thus, the technique is general and does not use previous knowledge about musical instruments. In the second approach, an ASA extension is studied with a dynamic neural model which is able to separate the different musical instruments taking a priori unknown perceptual elements as a point of departure. Applying an inverse transformation to the output of the model, the different contributions to the mixture can be recovered again in the time domain.

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Virtual Analog (VA) modeling aims to simulate the behavior of hardware circuits via algorithms to replicate their tone digitally. Dynamic Range Compressor (DRC) is an audio processing module that controls the dynamics of a track by reducing and amplifying the volumes of loud and quiet sounds, which is essential in music production. In recent years, neural-network-based VA modeling has shown great potential in producing high-fidelity models. However, due to the lack of data quantity and diversity, their generalization ability in different parameter settings and input sounds is still limited. To tackle this problem, we present Solid State Bus-Comp, the first large-scale and diverse dataset for modeling the classical VCA compressor — SSL 500 G-Bus. Specifically, we manually collected 175 unmastered songs from the Cambridge Multitrack Library. We recorded the compressed audio in 220 parameter combinations, resulting in an extensive 2528-hour dataset with diverse genres, instruments, tempos, and keys. Moreover, to facilitate the use of our proposed dataset, we conducted benchmark experiments in various open-sourced black-box and grey-box models, as well as white-box plugins. We also conducted ablation studies in different data subsets to illustrate the effectiveness of the improved data diversity and quantity. The dataset and demos are on our project page: https: //www.yichenggu.com/SolidStateBusComp/.

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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.

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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.

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The process of creating drum sounds has seen significant evolution in the past decades. The development of analogue drum synthesizers, such as the TR-808, and modern sound design tools in Digital Audio Workstations led to a variety of drum timbres that defined entire musical genres. Recently, drum synthesis research has been revived with a new focus on training generative neural networks to create drum sounds. Different interfaces have previously been proposed to control the generative process, from low-level latent space navigation to high-level semantic feature parameterisation, but no comprehensive analysis has been presented to evaluate how each approach relates to the creative process. We aim to evaluate how different interfaces support creative control over drum generation by conducting a user study based on the Creative Support Index. We experiment with both a supervised method that decodes semantic latent space directions and an unsupervised Closed-Form Factorization approach from computer vision literature to parameterise the generation process and demonstrate that the latter is the preferred means to control a drum synthesizer based on the StyleGAN2 network architecture.

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This paper presents an examination of State Space Models (SSM) and Koopman-based deep learning methods for modelling the dynamics of both linear and non-linear stiff strings. Through experiments with datasets generated under different initial conditions and sample rates, we assess the capacity of these models to accurately model the complex behaviours observed in string dynamics. Our findings indicate that our proposed Koopman-based model performs as well as or better than other existing approaches in nonlinear cases for long-sequence modelling. We inform the design of these architectures with the structure of the problems at hand. Although challenges remain in extending model predictions beyond the training horizon (i.e., extrapolation), the focus of our investigation lies in the models’ ability to generalise across different initial conditions within the training time interval. This research contributes insights into the physical modelling of dynamical systems (in particular those addressing musical acoustics) by offering a comparative overview of these and previous methods and introducing innovative strategies for model improvement. Our results highlight the efficacy of these models in simulating non-linear dynamics and emphasise their wide-ranging applicability in accurately modelling dynamical systems over extended sequences.

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In this paper, we propose the use of real-valued Linear Recurrent Units (LRUs) for black-box modeling of audio circuits. A network architecture composed of real LRU blocks interleaved with nonlinear processing stages is proposed. Two case studies are presented, a second-order diode clipper and an overdrive distortion pedal. Furthermore, we show how to integrate the antiderivative antialiaisng technique into the proposed method, effectively lowering oversampling requirements. Our experiments show that the proposed method generates models that accurately capture the nonlinear dynamics of the examined devices and are highly efficient, which makes them suitable for real-time operation inside Digital Audio Workstations.

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Recent advancements in generative audio synthesis have allowed for the development of creative tools for generation and manipulation of audio. In this paper, a strategy is proposed for the synthesis of drum sounds using generative adversarial networks (GANs). The system is based on a conditional Wasserstein GAN, which learns the underlying probability distribution of a dataset compiled of labeled drum sounds. Labels are used to condition the system on an integer value that can be used to generate audio with the desired characteristics. Synthesis is controlled by an input latent vector that enables continuous exploration and interpolation of generated waveforms. Additionally we experiment with a training method that progressively learns to generate audio at different temporal resolutions. We present our results and discuss the benefits of generating audio with GANs along with sound examples and demonstrations.

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