Evolutionary Sound Matching: A Test Methodology and Comparative Study

Schmitz, Adeline; Hefazi, Hamid

doi:10.1109/icmla.2007.34

Cited by 7 publications

(2 citation statements)

References 40 publications

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“…To demonstrate the validity of our approach, we have tested our algorithm with two types of target sounds: contrived sounds [23] and recorded sounds. The first category of sounds are sounds generated by Pd Patches.…”

Section: Methodsmentioning

confidence: 99%

Automatic design of sound synthesizers as pure data patches using coevolutionary mixed-typed cartesian genetic programming

Macret

Pasquier

2014

Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation

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A sound synthesizer can be defined as a program that takes a few input parameters and returns a sound. The general sound synthesis problem could then be formulated as: given a sound (or a set of sounds) what program and set of input parameters can generate that sound (set of sounds)? We propose a novel approach to tackle this problem in which we represent sound synthesizers using Pure Data (Pd), a graphic programming language for digital signal processing. We search the space of possible sound synthesizers using Coevolutionary Mixed-typed Cartesian Genetic Programming (MT-CGP), and the set of input parameters using a standard Genetic Algorithm (GA). The proposed algorithm coevolves a population of MT-CGP graphs, representing the functional forms of synthesizers, and a population of GA chromosomes, representing their inputs parameters. A fitness function based on the Mel-frequency Cepstral Coefficients (MFCC) evaluates the distance between the target and produced sounds. Our approach is capable of suggesting novel functional forms and input parameters, suitable to approximate a given target sound (and we hope in future iterations a set of sounds). Since the resulting synthesizers are presented as Pd patches, the user can experiment, interact with, and reuse them.

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Section: Methodsmentioning

confidence: 99%

Automatic design of sound synthesizers as pure data patches using coevolutionary mixed-typed cartesian genetic programming

Macret

Pasquier

2014

Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation

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“…The application of evolutionary computation for FM synthesis parameter matching is a well-explored area and has proven to be an effective and accurate technique for both simple and more advanced FM synthesis structures. [5][6][7] However, a major disadvantage is that they require considerable computational time, and researchers have highlighted a desire for ways to optimize this approach. 8 Therefore, this work does not advance the state-of-the-art in evolutionary computation for audio synthesis matching, rather, it proposes algorithms that accelerate existing methods by providing a data-parallel GPU design that overcomes challenges faced by the distinct architecture.…”

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confidence: 99%

Survival of the synthesis—GPU accelerating evolutionary sound matching

Renney

Gaster

Mitchell

2022

Concurrency and Computation

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Manually configuring synthesizer parameters to reproduce a particular sound is a complex and challenging task. Researchers have previously used different optimization algorithms, including evolutionary algorithms to find optimal sound matching solutions. However, a major drawback to these algorithms is that they typically require large amounts of computational resources, making them slow to execute. This article proposes an optimized design for matching sounds generated by frequency modulation (FM) audio synthesis using the graphics processing unit (GPU). A benchmarking suite is presented for profiling the performance of three implementations: serial CPU, data-parallel CPU, and data-parallel GPU. Results have been collected and discussed from a high-end NVIDIA desktop and a mid-range AMD laptop. Using the default configuration for simple FM, the GPU accelerated design had a speedup of 128× over the naive serial implementation and 8.88× over the parallel CPU version on a desktop with an Intel i7 9800X CPU and NVIDIA RTX GeForce 2080Ti GPU. Furthermore, the relative speedup over the naive serial implementation continues to increase beyond simple FM to more advanced structures. Further observations include comparisons between integrated and discrete GPUs, toggling optimizations, and scaling evolutionary strategy population size.

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Overlap-Based Similarity Metrics for Motif Search in DNA Sequences

Thanh

Wang

2009

Neural Information Processing

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Evolutionary Sound Matching: A Test Methodology and Comparative Study

Cited by 7 publications

References 40 publications

Automatic design of sound synthesizers as pure data patches using coevolutionary mixed-typed cartesian genetic programming

Automatic design of sound synthesizers as pure data patches using coevolutionary mixed-typed cartesian genetic programming

Survival of the synthesis—GPU accelerating evolutionary sound matching

Overlap-Based Similarity Metrics for Motif Search in DNA Sequences

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