Automatic Synthesizer Preset Generation with <i>PresetGen</i>

Tatar, Kıvanç; Macret, Matthieu; Pasquier, Philippe

doi:10.1080/09298215.2016.1175481

Cited by 12 publications

(18 citation statements)

References 15 publications

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“…Other works that are strictly related to computational sound design are reported in [29], [30], [31], where genetic algorithms are used to estimate coefficients of synthesizers. Specifically, in [29] the concept of contrived and non-contrived matching is introduced, which is used also in this work.…”

Section: Introductionmentioning

confidence: 99%

“…The paper evaluates results in terms of spectral Euclidean distance. The envelope Euclidean distance is proposed in [31] and the relative spectral error in [30]. In [32] linear regression is used to estimate parameters using the Source-to-Distortion Ratio (SDR) [33], however the definition is not clear and there is no detail on how this can be applied to the regression scenario described in the paper.…”

Section: Introductionmentioning

confidence: 99%

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A Multi-Stage Algorithm for Acoustic Physical Model Parameters Estimation

Gabrielli

Tomassetti

Squartini

et al. 2019

IEEE/ACM Trans. Audio Speech Lang. Process.

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One of the challenges in computational acoustics is the identification of models that can simulate and predict the physical behavior of a system generating an acoustic signal. Whenever such models are used for commercial applications an additional constraint is the time-to-market, making automation of the sound design process desirable. In previous works, a computational sound design approach has been proposed for the parameter estimation problem involving timbre matching by deep learning, which was applied to the synthesis of pipe organ tones. In this work we refine previous results by introducing the former approach in a multi-stage algorithm that also adds heuristics and a stochastic optimization method operating on objective cost functions based on psychoacoustics. The optimization method shows to be able to refine the first estimate given by the deep learning approach and substantially improve the objective metrics, with the additional benefit of reducing the sound design process time. Subjective listening tests are also conducted to gather additional insights on the results.Index Terms-physics-based acoustic modeling, neural networks, computational sound design, iterative optimization

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Multi-Stage Algorithm for Acoustic Physical Model Parameters Estimation

Gabrielli

Tomassetti

Squartini

et al. 2019

IEEE/ACM Trans. Audio Speech Lang. Process.

View full text Add to dashboard Cite

show abstract

“…More recent work has targeted specific FM software synths [16] and modular software synths [9,14]. The former case looks at relatively simple software synthesizers, comparing the GA's ability to match timbre with that of human programmers, finding that the computer finds more accurate results in significantly less time.…”

Section: Introductionmentioning

confidence: 99%

“…While other research has proven largely successful in supporting individual instruments [9,14,16], this work's primary challenge is designing a flexible optimization algorithm capable of handling the variety and number of parameters that may be available (simple FM synthesis models, or effects devices, often have a few parameters while commercial implementations, such as Native Instruments Massive and FM8, have over a thousand parameters), without using privileged knowledge about the synthesis model in question or specific parameter assignments.…”

Section: Introductionmentioning

confidence: 99%

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Play it Again: Evolved Audio Effects and Synthesizer Programming

Smith

2017

Computational Intelligence in Music, Sound, Art and Design

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Abstract. Automatic programming of sound synthesizers and audio devices to match a given, desired sound is examined and a Genetic Algorithm (GA) that functions independent of specific synthesis techniques is proposed. Most work in this area has focused on one synthesis model or synthesizer, designing the GA and tuning the operator parameters to obtain optimal results. The scope of such inquiries has been limited by available computing power, however current software (Ableton Live, herein) and commercially available hardware is shown to quickly find accurate solutions, promising a practical application for music creators. Both software synthesizers and audio effects processors are examined, showing a wide range of performance times (from seconds to hours) and solution accuracy, based on particularities of the target devices. Random oscillators, phase synchronizing, and filters over empty frequency ranges are identified as primary challenges for GA based optimization.

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SerumRNN: Step by Step Audio VST Effect Programming

Mitcheltree

Koike

2021

Artificial Intelligence in Music, Sound, Art and Design

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Learning to program an audio production VST synthesizer is a time consuming process, usually obtained through inefficient trial and error and only mastered after years of experience. As an educational and creative tool for sound designers, we propose SerumRNN : a system that provides step-by-step instructions for applying audio effects to change a user's input audio towards a desired sound. We apply our system to Xfer Records Serum: currently one of the most popular and complex VST synthesizers used by the audio production community. Our results indicate that SerumRNN is consistently able to provide useful feedback for a variety of different audio effects and synthesizer presets. We demonstrate the benefits of using an iterative system and show that SerumRNN learns to prioritize effects and can discover more efficient effect order sequences than a variety of baselines.

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Automatic Synthesizer Preset Generation with PresetGen

Cited by 12 publications

References 15 publications

A Multi-Stage Algorithm for Acoustic Physical Model Parameters Estimation

A Multi-Stage Algorithm for Acoustic Physical Model Parameters Estimation

Play it Again: Evolved Audio Effects and Synthesizer Programming

SerumRNN: Step by Step Audio VST Effect Programming

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