Arabic audio clips: Identification and discrimination of authentic Cantillations from imitations

Lataifeh, Mohammed; Elnagar, Ashraf; Shahin, Ismail; Nassif, Ali Bou

doi:10.1016/j.neucom.2020.07.099

Cited by 28 publications

(20 citation statements)

References 51 publications

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“…The curated audio samples averaged 10 seconds in length and for the actual deployment in our published experiments [3] they were pre-processed by acoustic analysis (Seewave and tuneR packages), with an analyzed frequency range of 80-280 hz (human vocal range). The clips were then saved in WAV format, mono 16 bits to ensure uniformity.…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

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Ar-DAD: Arabic diversified audio dataset

Lataifeh

Elnagar

2020

Data in Brief

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The automatic identification and verification of speakers through representative audio continue to gain the attention of many researchers with diverse domains of applications. Despite this diversity, the availability of classified and categorized multi-purpose Arabic audio libraries is scarce. Therefore, we introduce a large Arabic-based audio clips dataset (15810 clips) of 30 popular reciters cantillating 37 chapters from the Holy Quran. These chapters have a variable number of verses saved to different subsequent folders, where each verse is allocated one folder containing 30 audio clips for the declared reciters covering the same textual content. An additional 397 audio clips for 12 competent imitators of the top reciters are collected based on popularity and number of views/downloads to allow for cross-comparison of text, reciters, and authenticity. Based on the volume, quality, and rich diversity of this dataset we anticipate a wide range of deployments for speaker identification, in addition to setting a new direction for the structure and organization of similar large audio clips dataset.

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Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

“…In terms of the distribution of imitation clips per reciters, we manage to collect imitations for 19 reciters out of the main 30 reciters as explored in Table 1 for further details on the reciters list and deployment procedures please see in [2] , [3] . Nonetheless, the distribution of imitation clips per reciter is shown in Fig.…”

Section: Data Descriptionmentioning

confidence: 99%

Ar-DAD: Arabic diversified audio dataset

Lataifeh

Elnagar

2020

Data in Brief

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“…When the degree of data dispersion is greater, the greater the gray level of the data, the lower the prediction accuracy. Lataifeh [16] found that the function definition of the regression model is similar to that of the classification model. e main difference is that the regression model uses continuous predicted values, while the classification model uses discrete predicted values (such as class labels).…”

Section: Related Workmentioning

confidence: 95%

Multimedia Vocal Performance Automation Evaluation Model Based on RBF Network

Wang

2022

Mathematical Problems in Engineering

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Aiming at the problems of the radial basis network model, this paper proposes a multimedia vocal singing automation evaluation network model, combined with the characteristics of multimedia modeling innovation design, and proposes a two-level comprehensive model. First of all, the theory and algorithm of analytic hierarchy process and radial basis function network are researched and analyzed, and the RBF is predicted for the mature area of multimedia development based on the three indicators of the total amount of classified vocals. The prediction scheme evaluation system is then used to fit the prediction data and influencing factors using the RBF network, and then the classified vocals are adjusted and synthesized hierarchically, and a multimedia vocal classification prediction model is established. Finally, this paper uses an example to verify the feasibility performance and prediction accuracy. Based on the above theory, the experiment uses VC++ 6.0 and Matlab 6.5 combined with database technology to initially realize the evaluation system and achieves a good evaluation effect. The simulation results show that three different algorithms are used to establish RBFO content prediction models. The correlation coefficient limit, root mean square error prediction, and relative analysis error (RED) reached 0.9937, 15.5095, and 8.216, respectively. At the same time, the evaluation results have high accuracy and credibility, which not only provide designers with ideas and improvement basis for innovative designs but also ensure design quality, improve design efficiency, and show that RBF networks have good generalization capabilities.

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“…Support vector machine "SVM" is another supervised machine learning algorithm that mainly used in classifications and can also be used in regression through some modifications [12].…”

Section: Support Vector Machinementioning

confidence: 99%

Death/Recovery Prediction for Covid-19 Patients using Machine Learning

Atef

Nassif

AbuTalib

et al. 2020

International Journal of Systems Applications, Engineering &Amp; Development

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Covid19 is a newly discovered corona virus that has been officially announced as a pandemic by the World Health Organization in March 2020. It is a new virus in the medical field that has no specific treatment and no vaccines until this moment. Covid19 is spreading very fast as the medical systems over the world are not able to hospitalize all the patients which lead into a significant increase in the number of the virus death. This work uses machine learning models to predict which patient has a higher probability of death. Three different algorithms such as multilayer perceptron, support vector machine and K nearest neighbor were used in this work. The accuracies achieved were between 92% to 100% with MLP, SVM and KNN. SVM achieved the highest accuracy. The models were evaluated through precision, accuracy, recall and F measure.

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Arabic audio clips: Identification and discrimination of authentic Cantillations from imitations

Cited by 28 publications

References 51 publications

Ar-DAD: Arabic diversified audio dataset

Ar-DAD: Arabic diversified audio dataset

Multimedia Vocal Performance Automation Evaluation Model Based on RBF Network

Death/Recovery Prediction for Covid-19 Patients using Machine Learning

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