Spectral features in the singing voice evaluation using simulated imperfections

Łazoryszczak, M.

doi:10.1016/j.procs.2020.09.146

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…Financial Data. On the basis of the analysis of distributed optical fiber sensor number structure and the construction of information flow model, the Atlas features of cloud resources are extracted, which can effectively reflect the internal inherent modal characteristics of the data by extracting the high-order spectral features of university financial system process operation data in blockchain environment [21]. By optimizing Atlas design, the Atlas information of high-order spectral features of data can be extracted, which can reduce the storage cost of data cloud resources and data redundancy.…”

Section: Extraction and Design Of Atlas Features Of Universitymentioning

confidence: 99%

A Secure Cloud Storage Algorithm for University Financial Data Based on the Blockchain Technology

Zhang

Geng

2022

Mobile Information Systems

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In order to increase the secure storage capacity of university financial system process operation data under the blockchain environment, a secure cloud storage algorithm of university financial data based on the blockchain technology is proposed. The blockchain storage structure model of university financial system process operation data is first constructed, and then the mapping method of Atlas features is adopted. Finally, the blockchain equilibrium configuration parameter analysis model of university financial system process operation data is established. According to the outcomes of the feature extraction process of cloud resource storage Atlas of university financial system process operation data, the fuzzy clustering method is implemented to comprehend the rational planning of cloud storage space. The resource cloud storage structure configuration of university financial system process operation data in cloud environment is realized by using the method of block link fusion and channel equilibrium configuration. The multi-layer modal structure decomposition and fuzzy clustering processing are carried out on university financial system process operation data storage information in blockchain environment by using the empirical mode decomposition method. According to the data graph clustering results, the cloud resource graph of university financial system process operation data becomes smooth in the adjacent wave domain through cloud information fusion and block clustering, which effectively reduces the data storage overhead and improves the secure cloud storage capability of university financial data. The simulation outcomes indicate that this approach can significantly increase the storage performance of process operation data of university financial system under blockchain environment, with better data classification storage, internal structure information fusion performance of university financial data, and lower storage overhead than other methods. We observed that this improvement, in terms of storage overhead costs, can be as high as 43.67% higher than the wavelet method and 30.45% higher than the mode decomposition approach.

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Section: Extraction and Design Of Atlas Features Of Universitymentioning

confidence: 99%

A Secure Cloud Storage Algorithm for University Financial Data Based on the Blockchain Technology

Zhang

Geng

2022

Mobile Information Systems

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“…Dziedāšanai nepieciešamās prasmes var tikt attīstītas un nostiprinātas ar dažādu mācību uzdevumu un vingrinājumu palīdzību, regulāru treniņu rezultātā (Vilde, 2013). Visbiežāk vingrinājumi tiek izvēlēti subjektīvi, atsaucoties uz katra pedagoga individuālo pieredzi un zināšanām, taču tos apvieno līdzīga izpratne par kvalitatīvu skaņveidi un balss skanējuma estētiku (Lazoryszczak, 2020). Iedziedāšanās vingrinājumi var tikt papildināti ar dziedātājam piemērotas stājas sagatavošanu, elpas un balss atbrīvošanas vingrinājumiem.…”

Section: Teorētiskais Apskats Theoretical Reviewunclassified

The Potential and Working Vocal Range of Girls Aged 6 to 12

Ivane

Trinīte

2021

SIE

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Aim of this study is to determine the potential and working vocal range in the results of objective and subjective voice range evaluation in elementary school girls – solo singers. Theoretical part contains review of other authors’ studies related to child’s voice range from 6 to 12 years. Practical part of the study offered making of the phonetogram by using Voice Range Profile software (CSL 4500, Kay Pentax, USA) as an objective method for voice range evaluation. For subjective voice analysis, a voice range evaluation in form of vocal exercises was performed. It is understood that the objective voice evaluation reflects the potential of girls’ voice range, while the subjective evaluation – current working voice range. Fourteen girls attending solo singing classes were part of this study (medium age: 9.14 years; SD: 1.79 years; age range: 6-12 years). Three study groups were formed: 6-8 year old girls (n = 5); 9-10 year old girls (n=5); 11-12 year old girls (n=4). In 6-8 year old girls, VRP was from sol# (204.31 Hz, SD: 26, 05) to do3 (1047.72, SD: 409.70), but in subjective voice range evaluation, it was from la (220.28 Hz, SD: 18.88) to fa2 (775.90 Hz, SD: 70.64). In the group from 9-10 year old girls VRP range was from fa (171.25 Hz, SD: 16.03) to re#3 (1224.47 Hz, SD: 156.12), but current working voice range was from sol (208.79 Hz, SD: 24.90) to sol#2 (842.20 Hz, SD: 132.80). In 11-12 years old girls objective VRP results were fa# (186.54 Hz, SD: 27.76) to re#3 (1277.61 Hz, SD: 80.09), while from sol (208.23 Hz, SD: 12.16) to si2 (933.46 Hz, SD: 175.22) the work range was. In discussion part findings by other authors are compared to the objective and subjective voice range data acquired in this study. It is concluded that potential child’s voice range in girls 6-12 years of age exceeds two octaves yet working range reaches up to two octaves.

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“…In order to solve the problems existing in the evaluation of subjective auditory perception, and to make the evaluation results more accurately reflect the functional state of people's voice during vocalization, it is necessary to find a new evaluation method, which takes the physical characteristics of the auditory as the standard and is not subject to subjective factors. With the rapid development of electronic technology and computer technology, the interaction between computers and other electronic products and human beings has become more and more extensive and in-depth [8][9][10][11][12][13][14]. ey penetrate into almost every aspect of human life, give human beings great help, and also work for the voice.…”

Section: Introductionmentioning

confidence: 99%

Voice Recognition and Evaluation of Vocal Music Based on Neural Network

Wang

2022

Computational Intelligence and Neuroscience

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Artistic voice is the artistic life of professional voice users. In the process of selecting and cultivating artistic performing talents, the evaluation of voice even occupies a very important position. Therefore, an appropriate evaluation of the artistic voice is crucial. With the development of art education, how to scientifically evaluate artistic voice training methods and fairly select artistic voice talents is an urgent need for objective evaluation of artistic voice. The current evaluation methods for artistic voices are time-consuming, laborious, and highly subjective. In the objective evaluation of artistic voice, the selection of evaluation acoustic parameters is very important. Attempt to extract the average energy, average frequency error, and average range error of singing voice by using speech analysis technology as the objective evaluation acoustic parameters, use neural network method to objectively evaluate the singing quality of artistic voice, and compare with the subjective evaluation of senior professional teachers. In this paper, voice analysis technology is used to extract the first formant, third formant, fundamental frequency, sound range, fundamental frequency perturbation, first formant perturbation, third formant perturbation, and average energy of singing acoustic parameters. By using BP neural network methods, the quality of singing was evaluated objectively and compared with the subjective evaluation of senior vocal professional teachers. The results show that the BP neural network method can accurately and objectively evaluate the quality of singing voice by using the evaluation parameters, which is helpful in scientifically guiding the selection and training of artistic voice talents.

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Spectral features in the singing voice evaluation using simulated imperfections

Cited by 3 publications

References 9 publications

A Secure Cloud Storage Algorithm for University Financial Data Based on the Blockchain Technology

A Secure Cloud Storage Algorithm for University Financial Data Based on the Blockchain Technology

The Potential and Working Vocal Range of Girls Aged 6 to 12

Voice Recognition and Evaluation of Vocal Music Based on Neural Network

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