Landmark-based music recognition system optimisation using genetic algorithms

Gutiérrez, Salvador; García, Salvador

doi:10.1007/s11042-015-2963-0

Cited by 5 publications

(4 citation statements)

References 35 publications

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“…Each module is optimized separately, resulting in a tight connection of input and output, detecting more errors, and wasting more application resources. Studies have shown that learning features play an important role in speech recognition systems [ 10 , 22 ]. The deep network combines feature learning and process optimization, thereby reducing the number of modules in the word recognition system.…”

Section: Research Methods Of Speech Recognition Technology In Music S...mentioning

confidence: 99%

“…Rectified linear units (ReLUs) are expressed as Computational Intelligence and Neuroscience y � f(x) � max(0, x). (10) e most commonly used function in neural networks is the sigmoid function. e derivation of the sigmoid function is very simple, but when the independent variable is far away from the origin of the coordinate, the slope of the function decreases rapidly and tends to 0, resulting in "gradient disappearance."…”

Section: Full Convolutionmentioning

confidence: 99%

“…They extracted original features from each piece of music and cleared rules are transformed into fuzzy rules with confidence factors [ 9 ]. The spectrum markers is intended to achieve robustness, so Gutiérrez and Garcia define and established an appropriate fitness evaluation method by converting its highly correlated parameters into various genes [ 10 ]. However, due to the lack of relevant voice data in these studies, there are also some controversies in the methods used, resulting in the related results not being recognized by the public.…”

Section: Introductionmentioning

confidence: 99%

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Improvement of Speech Recognition Technology in Piano Music Scene Based on Deep Learning of Internet of Things

Bai

2022

Computational Intelligence and Neuroscience

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The main goal of speech recognition technology is to use computers to convert human analog speech signals into computer-generated signals, such as behavior patterns or binary codes. Different from speaker identification and speaker confirmation, the latter attempts to identify or confirm the speaker who uttered the speech rather than the lexical content contained in it. The short-term idea is that it should be able to record the musical sound played by the user with a certain musical instrument, then extract the note and duration information from it, and finally generate the corresponding MID file according to the MIDI standard, which can set the type of musical instrument in advance to complete the function of musical sound transformation, such as playing with a harmonica, and playing the MID at the end is the piano sound. With the rapid development of the mobile Internet, fields such as machine learning, electronic communication, and navigation have placed high demands on real-time and standard text recognition technology. This paper merges the sound of visual music into text-based data set training, uses the exported scanner features for model training, uses the model to extract features, then uses the features for prior training, and then uses pretraining. DNN results show that the combined training of target prevention and expansion plans, by replacing long-term and short-term memory networks, end-to-end speech recognition programs, and behavioral tests organized by mobile devices, can provide a larger receptive field combined with expanded convolution instead of long and short periods. The experimental results show that when the input sampling point is 2400, it can be seen that the convergence speed of the model becomes slower with more than 90 iterations and the loss of the model on the verification set increases with the increase in the number of iterations. This shows that the model in this paper can fully meet the needs of speech recognition in piano music scenes.

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Section: Research Methods Of Speech Recognition Technology In Music S...mentioning

confidence: 99%

Section: Full Convolutionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improvement of Speech Recognition Technology in Piano Music Scene Based on Deep Learning of Internet of Things

Bai

2022

Computational Intelligence and Neuroscience

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“…Suitable for exploring evolutionary scenarios in a number of domains, genetic programming has proved fruitful in music-generative tasks, allowing for the rapid replaying of the memetic processes hypothesised to have underpinned "real" music-cultural evolution. Beyond music synthesis, GAs have been used for music-analytical purposes (Rafael et al, 2009;Geetha Ramani & Priya, 2019); and for emotion-, genre-and piece/song-recognition tasks (Gutiérrez & García, 2016). In some music-generative systems -such as DarwinTunes -selection is devolved to human choice, the power and reach of the internet making such crowd-based evaluations of candidate patterns relatively easy to solicit.…”

Section: Genetic/evolutionary Algorithmsmentioning

confidence: 99%

Music in Evolution and Evolution in Music

Jan

2022

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Music in Evolution and Evolution in Music by Steven Jan is a comprehensive account of the relationships between evolutionary theory and music. Examining the ‘evolutionary algorithm’ that drives biological and musical-cultural evolution, the book provides a distinctive commentary on how musicality and music can shed light on our understanding of Darwin’s famous theory, and vice-versa. Comprised of seven chapters, with several musical examples, figures and definitions of terms, this original and accessible book is a valuable resource for anyone interested in the relationships between music and evolutionary thought. Jan guides the reader through key evolutionary ideas and the development of human musicality, before exploring cultural evolution, evolutionary ideas in musical scholarship, animal vocalisations, music generated through technology, and the nature of consciousness as an evolutionary phenomenon. A unique examination of how evolutionary thought intersects with music, Music in Evolution and Evolution in Music is essential to our understanding of how and why music arose in our species and why it is such a significant presence in our lives.

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