Using Classification Algorithms for Turkish Music Makam Recognition

Öztürk, Övünç; Abidin, Didem; Özacar, Tuğba

doi:10.15317/scitech.2018.139

Cited by 5 publications

(5 citation statements)

References 16 publications

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“…The speech recognition system is mainly composed of four modules: audio feature output, audio model, language model, and decoder. The audio feature output unit processes the predefined speech signal, first changes the input signal from the time zone to the frequency band, and then uses an appropriate method to remove the best possible feature of the audio model [ 16 , 17 ]. The audio model must input the feature vector mentioned above to calculate the matching of the speech signal and the phoneme.…”

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

confidence: 99%

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%

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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“…Two hundred of samples were selected. and determine the value of parameters through the cross test, as shown in Tables 3 and 4. Recognition rate/% 2 -1 90.11 2 91.23 2 2 93.87 2 3 92.18 2 4 92.09 2 5 91.63 2 6 91.29 2 7 90.88 2 8 90.64 2 9 89.72 2 10 88.33 Table 3: The influence of the value of on the recognition rate when the value of 2 takes 2.…”

Section: Resultsmentioning

confidence: 99%

“…Recognition rate /% 2 0 88.64 2 1 89.72 2 2 90.07 2 3 91.22 2 4 92.08 2 5 93.09 2 6 93.87 2 7 93.06 2 8 92.18 2 9 91.53 2 10 90.27 Table 4: The influence of the value of 2 on the recognition rate when the value of takes 4. Tables 3 and 4 that the recognition rate of SVM was the highest when = 4 and 2 = 2 6 .…”

Section: Resultsmentioning

confidence: 99%

“…The combination of them could improve the recognition rate of HMM by 4% -5% while maintaining the same calculation speed as HMM. Abidin et al [2] recognized a Turkish music data set, SymbTr, with ten machine learning algorithms, and found that the performance of the algorithms was between 82% and 88%. Rao et al [3] studied chord recognition.…”

Section: Introductionmentioning

confidence: 99%

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Research on Recognition and Classification of Folk Music Based on Feature Extraction Algorithm

Wang¹

2020

IJCAI

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“…yaptıkları çalışmada Türk müziği eserleri üzerinde dil tanıma uygulamasına çok benzer ve değişik bir uygulama olan makam tanıma çalışması gerçekleştirmişlerdir. Farklı Türk müziği eserlerinin farklı makama ait özellik göstermesinde yola çıkılarak Türk makam tanıma konusunda dil tanıma çalışmasına yakın bir faklı çalışma gerçekleştirmişlerdir [25]. Basile vd.…”

Section: Li̇teratür çAlişmalari (Literature Studies)unclassified

Döküman dili tanıma için içerik bağımsız yeni bir yaklaşım: Açı Örüntüler

NOYAN

KUNCAN

Tekin

et al. 2022

Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi

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 Language Identification in text mining is the process of detecting the natural language in which a document or part of it is written  The proposed angle pattern method is used for feature extraction from texts  The performance results of the proposed language recognition method in four different data sets are between 93,31% and 99,39%Language identification (LI) in text mining is the process of detecting the natural language in which a document or part of it is written. LI aims to mimic a human's ability to recognize certain languages from text by computer algorithms. LI can be defined as a classification problem subject based on the information used in word or character size for any document. When the literature is examined for LI application, it is seen that various linguistic or statistical-based approaches are used. Linguistic methods are methods that perform LI according to a special word or character of a language. These methods are applied based on the special rules of the languages. When we look at the statistical methods, it shows that the words or characters that make up the language depend on their frequency and distribution. The statistical approaches used are contentindependent methods. The semantic context of the text is not concerned with its content. According to linguistic methods, it does not provide sufficient information about the content of the text. The proposed model in this study is a statistical approach. Figure A. Proposed block diagram for LI Purpose: In this study, a new LI approach using the angle information between the UTF-8 values of the characters in the text is proposed. The proposed angle pattern method is used for feature extraction from texts.Angle patterns method is a statistical approach. In the angle method, there are two distance parameters, R and L, which express which neighborhood to look at from the reference point to the left and right. Theory and Methods:To test the proposed approach, four datasets, two created by the authors and two publicly available on the Internet, were used. By using the features obtained by the angle pattern method, classification process was carried out with different machine learning methods such as Random Forest, Support Vector Machine, Linear Discriminant Analysis, Naive Bayes and K-nearest neighbor. Language identification performance results determined from four different data sets were observed as 96.81%, 99.39%, 93.31% and 98.60%, respectively. Results:According to the performance results achieved as a result of the study, it has been determined that the proposed angle pattern method provides important distinguishing information in language identification application. It is thought that the proposed approach in this study can be used in many different text mining applications such as spam recognition, text categorization, as well as LI application.

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Using Classification Algorithms for Turkish Music Makam Recognition

Cited by 5 publications

References 16 publications

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

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

Research on Recognition and Classification of Folk Music Based on Feature Extraction Algorithm

Döküman dili tanıma için içerik bağımsız yeni bir yaklaşım: Açı Örüntüler

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