Modeling plate and spring reverberation using a DSP-informed deep neural network

Plate and spring reverberators are electromechanical systems first used and researched as means to substitute real room reverberation. Currently, they are often used in music production for aesthetic reasons due to their particular sonic characteristics. The modeling of these audio processors and their perceptual qualities is difficult since they use mechanical elements together with analog electronics resulting in an extremely complex response. Based on digital reverberators that use sparse FIR filters, we propose a signal processing-informed deep learning architecture for the modeling of artificial reverberators. We explore the capabilities of deep neural networks to learn such highly nonlinear electromechanical responses and we perform modeling of plate and spring reverberators. In order to measure the performance of the model, we conduct a perceptual evaluation experiment and we also analyze how the given task is accomplished and what the model is actually learning

Deep Learning for Audio Effects Modeling

PhD Thesis.Audio effects modeling is the process of emulating an audio effect unit and seeks to recreate the sound, behaviour and main perceptual features of an analog reference device. Audio effect units are analog or digital signal processing systems that transform certain characteristics of the sound source. These transformations can be linear or nonlinear, time-invariant or time-varying and with short-term and long-term memory. Most typical audio effect transformations are based on dynamics, such as compression; tone such as distortion; frequency such as equalization; and time such as artificial reverberation or modulation based audio effects. The digital simulation of these audio processors is normally done by designing mathematical models of these systems. This is often difficult because it seeks to accurately model all components within the effect unit, which usually contains mechanical elements together with nonlinear and time-varying analog electronics. Most existing methods for audio effects modeling are either simplified or optimized to a very specific circuit or type of audio effect and cannot be efficiently translated to other types of audio effects. This thesis aims to explore deep learning architectures for music signal processing in the context of audio effects modeling. We investigate deep neural networks as black-box modeling strategies to solve this task, i.e. by using only input-output measurements. We propose different DSP-informed deep learning models to emulate each type of audio effect transformations. Through objective perceptual-based metrics and subjective listening tests we explore the performance of these models when modeling various analog audio effects. Also, we analyze how the given tasks are accomplished and what the models are actually learning. We show virtual analog models of nonlinear effects, such as a tube preamplifier; nonlinear effects with memory, such as a transistor-based limiter; and electromechanical nonlinear time-varying effects, such as a Leslie speaker cabinet and plate and spring reverberators. We report that the proposed deep learning architectures represent an improvement of the state-of-the-art in black-box modeling of audio effects and the respective directions of future work are given

Allpass Feedback Delay Networks

In the 1960s, Schroeder and Logan introduced delay line-based allpass filters, which are still popular due to their computational efficiency and versatile applicability in artificial reverberation, decorrelation, and dispersive system design. In this work, we extend the theory of allpass systems to any arbitrary connection of delay lines, namely feedback delay networks (FDNs). We present a characterization of uniallpass FDNs, i.e., FDNs, which are allpass for an arbitrary choice of delays. Further, we develop a solution to the completion problem, i.e., given an FDN feedback matrix to determine the remaining gain parameters such that the FDN is allpass. Particularly useful for the completion problem are feedback matrices, which yield a homogeneous decay of all system modes. Finally, we apply the uniallpass characterization to previous FDN designs, namely, Schroeder's series allpass and Gardner's nested allpass for single-input, single-output systems, and, Poletti's unitary reverberator for multi-input, multi-output systems and demonstrate the significant extension of the design space

Modal Decomposition of Feedback Delay Networks

Feedback delay networks (FDNs) belong to a general class of recursive filters which are widely used in sound synthesis and physical modeling applications. We present a numerical technique to compute the modal decomposition of the FDN transfer function. The proposed pole finding algorithm is based on the Ehrlich-Aberth iteration for matrix polynomials and has improved computational performance of up to three orders of magnitude compared to a scalar polynomial root finder. We demonstrate how explicit knowledge of the FDN's modal behavior facilitates analysis and improvements for artificial reverberation. The statistical distribution of mode frequency and residue magnitudes demonstrate that relatively few modes contribute a large portion of impulse response energy

Designing sound : procedural audio research based on the book by Andy Farnell

In procedural media, data normally acquired by measuring something, commonly described as sampling, is replaced by a set of computational rules (procedure) that defines the typical structure and/or behaviour of that thing. Here, a general approach to sound as a definable process, rather than a recording, is developed. By analysis of their physical and perceptual qualities, natural objects or processes that produce sound are modelled by digital Sounding Objects for use in arts and entertainments. This Thesis discusses different aspects of Procedural Audio introducing several new approaches and solutions to this emerging field of Sound Design.Em Media Procedimental, os dados os dados normalmente adquiridos através da medição de algo habitualmente designado como amostragem, são substituídos por um conjunto de regras computacionais (procedimento) que definem a estrutura típica, ou comportamento, desse elemento. Neste caso é desenvolvida uma abordagem ao som definível como um procedimento em vez de uma gravação. Através da análise das suas características físicas e perceptuais , objetos naturais ou processos que produzem som, são modelados como objetos sonoros digitais para utilização nas Artes e Entretenimento. Nesta Tese são discutidos diferentes aspectos de Áudio Procedimental, sendo introduzidas várias novas abordagens e soluções para o campo emergente do Design Sonoro

A Corpus-assisted Discourse Analysis of Music-related Practices Discussed within Chipmusic.org

abstract: This study examined discussion forum posts within a website dedicated to a medium and genre of music (chiptunes) with potential for music-centered making, a phrase I use to describe maker culture practices that revolve around music-related purposes. Three research questions guided this study: (1) What chiptune-related practices did members of chipmusic.org discuss between December 30th, 2009 and November 13th, 2017? (2) What do chipmusic.org discussion forum posts reveal about the multidisciplinary aspects of chiptunes? (3) What import might music-centered making evident within chipmusic.org discussion forum posts hold for music education? To address these research questions, I engaged in corpus-assisted discourse analysis tools and techniques to reveal and analyze patterns of discourse within 245,098 discussion forum posts within chipmusic.org. The analysis cycle consisted of (a) using corpus analysis techniques to reveal patterns of discourse across and within data consisting of 10,892,645 words, and (b) using discourse analysis techniques for a close reading of revealed patterns. Findings revealed seven interconnected themes of chiptune-related practices: (a) composition practices, (b) performance practices, (c) maker practices, (d) coding practices, (e) entrepreneurial practices, (f), visual art practices, and (g) community practices. Members of chipmusic.org primarily discussed composing and performing chiptunes on a variety of instruments, as well as through retro computer and video game hardware. Members also discussed modifying and creating hardware and software for a multitude of electronic devices. Some members engaged in entrepreneurial practices to promote, sell, buy, and trade with other members. Throughout each of the revealed themes, members engaged in visual art practices, as well as community practices such as collective learning, collaborating, constructive criticism, competitive events, and collective efficacy. Findings suggest the revealed themes incorporated practices from a multitude of academic disciplines or fields of study for music-related purposes. However, I argue that many of the music-related practices people discussed within chipmusic.org are not apparent within music education discourse, curricula, or standards. I call for an expansion of music education discourse and practices to include additional ways of being musical through practices that might borrow from multiple academic disciplines or fields of study for music-related purposes.Dissertation/ThesisDoctoral Dissertation Music Education 201

Modelling, Simulation and Data Analysis in Acoustical Problems

Modelling and simulation in acoustics is currently gaining importance. In fact, with the development and improvement of innovative computational techniques and with the growing need for predictive models, an impressive boost has been observed in several research and application areas, such as noise control, indoor acoustics, and industrial applications. This led us to the proposal of a special issue about “Modelling, Simulation and Data Analysis in Acoustical Problems”, as we believe in the importance of these topics in modern acoustics’ studies. In total, 81 papers were submitted and 33 of them were published, with an acceptance rate of 37.5%. According to the number of papers submitted, it can be affirmed that this is a trending topic in the scientific and academic community and this special issue will try to provide a future reference for the research that will be developed in coming years