Derin Öğrenme İle Türkçe Müziklerden Müzik Türü Sınıflandırması

Hizlisoy, Serhat; Tüfekçi, Zekeriya

doi:10.31590/ejosat.898588

Cited by 2 publications

(2 citation statements)

References 15 publications

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“…Kendine özgü dili, yapısı ve anlatımıyla, insanların duygu ve düşünce alışverişinde bulunabilmelerini sağlayan önemli bir iletişim aracı olan müzik; sosyal, kültürel ve ekonomik açıdan üretildiği topluma ilişkin ipuçları barındırmaktadır. Günümüz iletişim teknolojilerinde yaşanan hızlı gelişmeler neticesinde dijitalleşen müzik dinleme eylemi ise her mekânda deneyimleyebildiğimiz gündelik hayat pratiklerimizden biri haline gelmiştir (Altan & Övür, 2020;Demir, 2022;Ergün, 2016;Gürkan, 2021;Hızlısoy & Tüfekçi, 2021;Kumpasoğlu, 2022). Demirtaş (2019), iletişim teknolojilerinin gelişmesi ve yaygınlaşmasıyla birlikte yaşamımıza yeni ve farklı kavramların girdiğini; bunlardan en önemlisinin yeni medya kavramı olduğunu ifade etmiştir.…”

Section: Introductionunclassified

“…Kültür endüstrisi ve teknoloji birbiriyle sürekli etkileşim içerisindedir ve buna bağlı olarak tarihsel süreç içinde müziğin metalaşma biçimi değişmiştir. Günümüz müzik endüstrisi ise yapay zekâ teknolojilerinin ve verinin işlenebilirliğinin artmasıyla birlikte dünya ekonomisi içinde önemli paya sahip olmaya başlamıştır (Hızlısoy & Tüfekçi, 2021).…”

Section: Introductionunclassified

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A Review of The Most Streamed Turkish Songs on Spotify

ÇINARDAL¹

2023

TurkishStudies

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The evolution of the culture industry and technology in the same direction has also affected the commodification of music as a means of communication. Today, when artificial intelligence is put to work, the music industry markets the service of listening to music freely with digital music platforms that provide real-time data flow. With this technology, which allows people to listen to music wherever and whenever they want, music has become an easily accessible element of consumption in daily life. As a current reflection, it can be said that people's music listening habits have gained a different dimension. Spotify is the most popular music service in the world using this technology. In this context, Spotify, which has the most users in the world, contains important clues about the music cultures of societies. This research aims to evaluate the most listened Turkish songs in Turkey in 2022 in the context of music sociology. The content analysis technique, one of the qualitative research methods, was used in the research. According to the specific parameters, the top 5 most listened songs in 2022 were analyzed. The frequency and percentage of descriptive statistics were used to analyse the data. According to the findings obtained from the research, it was concluded that the most listened-to song from the 2022 list is electro-pop (f= 132.812.414). It was determined that the other four songs were Turkish rap. Within the scope of the research, it has been tried to determine the current situation in terms of popular culture and music preference. Some determinations and evaluations about the Turkish rap genre, which is on the rise today, particularly thanks to the Z generation, are included.

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Section: Introductionunclassified

A Review of The Most Streamed Turkish Songs on Spotify

ÇINARDAL¹

2023

TurkishStudies

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Music Genre Recognition Based on Hybrid Feature Vector with Machine Learning Methods

HIZLISOY,

ARSLAN,

ÇOLAKOĞLU

2023

Çukurova Üniversitesi Mühendislik Fakültesi Dergisi

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Music genre recognition is one of the main problems in infotainment tools and music streaming service providers for different tasks such as music selection, classification, recommendation, and personal list creation. Automatic genre recognition systems can be useful for different music-based systems, especially different music platforms. Therefore, this study aimed to classify music genres using machine learning. In this context, GTZAN dataset consisting of 10 classes was used. In this dataset, data augmentation was applied by segmentation. Each record of 30 seconds was divided into 10 parts, increasing the number of samples in the dataset by a factor of 10. Then, features were extracted from the audio signals. The resulting features are chroma, harmony, mel frequency cepstral coefficients, perceptr, root mean square, roll-off, spectral centroid, tempo, and zero crossing rate. The types, variances, and averages of the obtained features were used. Thus, 57 features were obtained. This feature set was pre-processed by delimiting the decimal part, standardization, and label encoding. In the last step, classification was made with different machine learning methods and the results were compared. As a result of hyperparameter optimization in the Extra Tree model, 92.3% performance was achieved. Precision recall and f-score values are 92.4%, 92.3%, and 92.3%, respectively. As a result, an efficient and high-performance model in music genre recognition was created.

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Derin Öğrenme İle Türkçe Müziklerden Müzik Türü Sınıflandırması

Cited by 2 publications

References 15 publications

A Review of The Most Streamed Turkish Songs on Spotify

A Review of The Most Streamed Turkish Songs on Spotify

Music Genre Recognition Based on Hybrid Feature Vector with Machine Learning Methods

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