Evolution in Digital Audio Technology

Horner, Andrew

doi:10.1007/978-1-84628-600-1_3

Cited by 2 publications

(3 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The difficulty in sound matching is mainly getting the right timbre and pitch [12]. The temporal envelope can easily be obtained using, for example, an envelope follower.…”

Section: Pd Patch Designmentioning

confidence: 99%

“…Replicating sounds using synthesis techniques is a problem frequently addressed in the field of computer music [12]. A well-researched problem is the following: Given a target sound and a synthesizer, what set of input parameters can replicate or approximate this sound.…”

Section: Introductionmentioning

confidence: 99%

“…This search for input parameters is then a good candidate for an automatic optimization scheme. Thus, diverse optimization methods have been used for automatic calibration, such as Particle Swarm [11], HMM [34], Neural Nets [27], Cellular Automata [29] and Genetic Algorithms [12]. It has been suggested that evolutionary approaches such as Genetic Algorithms (GAs) are performing the best to matching musical instrument tones [27].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

View full text Add to dashboard Cite

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.

show abstract

“…The difficulty in sound matching is mainly getting the right timbre and pitch [12]. The temporal envelope can easily be obtained using, for example, an envelope follower.…”

Section: Pd Patch Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%