Matches in SemOpenAlex for { <https://semopenalex.org/work/W1970001848> ?p ?o ?g. }
Showing items 1 to 66 of
66
with 100 items per page.
- W1970001848 abstract "Rock and blues guitar players prefer the use of vacuum-tube amplifiers due to the harmonic structures developed when the amplifiers are overdriven. The disadvantages of vacuum tubes compared against solid-state implementations, such as power consumption, reliability, cost, etc., are far outweighed by the desirable sound characteristics of the overdriven vacuum-tube amplifier. There are many approaches to modeling vacuum-tube amplifier behaviors in solid-state implementations. These include a variety of both analog and digital techniques, some of which are judged to be good approximations to the tube sound. In this paper we present early results of experiments in using a neural network to model the distortion produced by an overdriven vacuum-tube amplifier. Our approach is to use artificial neural networks of the recurrent variety, specifically a Nonlinear AutoRegressive eXogenous (NARX) network, to capture the nonlinear, dynamic characteristics of vacuum-tube amplifiers. NARX networks of various sizes have been trained on data sets consisting of samples of both sinusoidal and raw electric guitar signals and the amplified output of those signals applied to a tube-based amplifier driven at various levels of saturation. Models are evaluated using both quantitative (e.g., RMS error) and qualitative (listening tests) assessment methods on data sets that were not used in the network training. Listening tests- considered by us to be the most important evaluation method-at this point in the work, are indicative of the potential for success in the modeling of a vacuum-tube amplifier using a recurrent neural network." @default.
- W1970001848 created "2016-06-24" @default.
- W1970001848 creator A5071041224 @default.
- W1970001848 creator A5075726230 @default.
- W1970001848 date "2013-04-01" @default.
- W1970001848 modified "2023-10-03" @default.
- W1970001848 title "A vacuum-tube guitar amplifier model using a recurrent neural network" @default.
- W1970001848 cites W2043333893 @default.
- W1970001848 cites W2057845019 @default.
- W1970001848 cites W2103525130 @default.
- W1970001848 cites W2113634576 @default.
- W1970001848 cites W2115072676 @default.
- W1970001848 cites W2137983211 @default.
- W1970001848 cites W2143908786 @default.
- W1970001848 cites W2171800554 @default.
- W1970001848 cites W4240172095 @default.
- W1970001848 doi "https://doi.org/10.1109/secon.2013.6567472" @default.
- W1970001848 hasPublicationYear "2013" @default.
- W1970001848 type Work @default.
- W1970001848 sameAs 1970001848 @default.
- W1970001848 citedByCount "14" @default.
- W1970001848 countsByYear W19700018482016 @default.
- W1970001848 countsByYear W19700018482018 @default.
- W1970001848 countsByYear W19700018482019 @default.
- W1970001848 countsByYear W19700018482020 @default.
- W1970001848 countsByYear W19700018482021 @default.
- W1970001848 countsByYear W19700018482022 @default.
- W1970001848 countsByYear W19700018482023 @default.
- W1970001848 crossrefType "proceedings-article" @default.
- W1970001848 hasAuthorship W1970001848A5071041224 @default.
- W1970001848 hasAuthorship W1970001848A5075726230 @default.
- W1970001848 hasConcept C103753734 @default.
- W1970001848 hasConcept C119599485 @default.
- W1970001848 hasConcept C121332964 @default.
- W1970001848 hasConcept C127413603 @default.
- W1970001848 hasConcept C194257627 @default.
- W1970001848 hasConcept C24326235 @default.
- W1970001848 hasConcept C24890656 @default.
- W1970001848 hasConcept C41008148 @default.
- W1970001848 hasConcept C46362747 @default.
- W1970001848 hasConceptScore W1970001848C103753734 @default.
- W1970001848 hasConceptScore W1970001848C119599485 @default.
- W1970001848 hasConceptScore W1970001848C121332964 @default.
- W1970001848 hasConceptScore W1970001848C127413603 @default.
- W1970001848 hasConceptScore W1970001848C194257627 @default.
- W1970001848 hasConceptScore W1970001848C24326235 @default.
- W1970001848 hasConceptScore W1970001848C24890656 @default.
- W1970001848 hasConceptScore W1970001848C41008148 @default.
- W1970001848 hasConceptScore W1970001848C46362747 @default.
- W1970001848 hasLocation W19700018481 @default.
- W1970001848 hasOpenAccess W1970001848 @default.
- W1970001848 hasPrimaryLocation W19700018481 @default.
- W1970001848 hasRelatedWork W141098887 @default.
- W1970001848 hasRelatedWork W2036939677 @default.
- W1970001848 hasRelatedWork W2132646971 @default.
- W1970001848 hasRelatedWork W2482316612 @default.
- W1970001848 hasRelatedWork W2899084033 @default.
- W1970001848 hasRelatedWork W2901211842 @default.
- W1970001848 hasRelatedWork W4212942227 @default.
- W1970001848 hasRelatedWork W4312998120 @default.
- W1970001848 hasRelatedWork W95099813 @default.
- W1970001848 hasRelatedWork W818797353 @default.
- W1970001848 isParatext "false" @default.
- W1970001848 isRetracted "false" @default.
- W1970001848 magId "1970001848" @default.
- W1970001848 workType "article" @default.