A musical information retrieval system for Classical Turkish Music makams

Kızrak, Merve Ayyüce; Bolat, Bulent

doi:10.1177/0037549717708615

Cited by 12 publications

(6 citation statements)

References 20 publications

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“…The various essential parameters of DNN, which are also familiar to ANN, are gradient descent, stochastic gradient descent, 11 back-propagation, 12 momentum, 13 learning rate (LR), 13 and weight decay. 14 The DBN 15,16 are a class of DNN algorithms modeled after the human brain, giving them a more exceptional ability to recognize patterns and process complex information. DBNs are algorithms that use probabilities and unsupervised learning to produce outputs.…”

Section: Deep Learningmentioning

confidence: 99%

“…The network structure of the deep belief network with restricted Boltzmann machine 16 F I G U R E 6 The schematic network structure of a recurrent neural network…”

Section: Deep Learningmentioning

confidence: 99%

“…The DBN 15,16 are a class of DNN algorithms modeled after the human brain, giving them a more exceptional ability to recognize patterns and process complex information. DBNs are algorithms that use probabilities and unsupervised learning to produce outputs.…”

Section: Introductionmentioning

confidence: 99%

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Convolutional neural network and its pretrained models for image classification and object detection: A survey

Jena

Nayak

Saxena

2021

Concurrency and Computation

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At present, in the age of computers and automation of services, deep learning (DL) technology, mainly the subset of machine learning (ML) and artificial intelligence (AI), is expressively used in innumerable domains of computer vision such as data analysis, image recognition, classification, natural language processing, and many more. It has become the foremost choice of researchers as of its effectiveness in producing decent results. This paper presents detailed and analytical literature starting from the very elementary level to the recent trends of this trending technology while focusing on the most used DL model, that is, convolutional neural network and its pretrained models for image classification and object detection. It also reviews diverse existing current literature based on this. Further, a brief introduction of AI, ML, and DL has also been presented, making the foundation for the readers. As pretrained models continuously give an upper edge to DL over ML and other technologies, 23 most popular pretrained models with their architectural diagrams have also been presented. This paper aims to summarize and analyze all the concepts used to formulate DL and its models. Also, we have emphasized more on the GoogleNet models and the entire Inception modules in detail. Finally, the fascinating applications and discussion on integral components of DL have been presented. This paper will definitely draw the attention of the students and researchers working in the area of DL and its models.

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Section: Deep Learningmentioning

confidence: 99%

“…The network structure of the deep belief network with restricted Boltzmann machine 16 F I G U R E 6 The schematic network structure of a recurrent neural network…”

Section: Deep Learningmentioning

confidence: 99%

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Convolutional neural network and its pretrained models for image classification and object detection: A survey

Jena

Nayak

Saxena

2021

Concurrency and Computation

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“…RBF network is widely used because of its good approximation characteristics. It is applied to the fields of function approximation, communication adaptive equalization, and pattern recognition [6][7][8][9]. However, there are still many problems to be solved in RBF network.…”

Section: Introductionmentioning

confidence: 99%

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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“…Secondly, the most obvious difference between TMM and western music is their tuning systems. Western * Correspondence: ismail@cs.deu.edu.tr music is formed on 12 equally spaced divisions (semitones) of an octave [12,13]; whereas, TMM is considered to be based on 24 notes, which are established on 53 equally spaced divisions per octave [14]. Although there are different theories trying to formalize the TMM tuning system, the most widely accepted one is the AEU (Arel-Ezgi-Uzdilek) theory [15].…”

Section: Introductionmentioning

confidence: 99%

Deep learning for Turkish makam music composition

Parlak¹,

Çebi²,

Işıkhan³

et al. 2021

Turk J Elec Eng & Comp Sci

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In this paper, we introduce a new deep-learning-based system that can compose structured Turkish makam music (TMM) in the symbolic domain. Presented artificial TMM composer (ATMMC) takes eight initial notes from a human user and completes the rest of the piece. The backbone of the composer system consists of multilayered long short-term memory (LSTM) networks. ATMMC can create pieces in Hicaz and Nihavent makams in Şarkı form, which can be viewed and played with Mus2, a notation software for microtonal music. Statistical analysis shows that pieces composed by ATMMC are approximately 84% similar to training data. ATMMC is an open-source project and can assist Turkish makam music enthusiasts with creating new pieces for professional, educational, or entertainment purposes.

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A musical information retrieval system for Classical Turkish Music makams

Cited by 12 publications

References 20 publications

Convolutional neural network and its pretrained models for image classification and object detection: A survey

Convolutional neural network and its pretrained models for image classification and object detection: A survey

Multimedia Vocal Performance Automation Evaluation Model Based on RBF Network

Deep learning for Turkish makam music composition

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