Bilgi Teorisi Metrikleri ve Ses Özelliklerini Kullanarak Dijital Müzik Kayıtlarındaki Farklı Performansların Karşılaştırmalı Analizi

Öz

Bu çalışma, bilgi teorisi ölçütleri ve ses özelliklerini kullanarak aynı müzik eserinin farklı performanslarının karşılaştırmalı bir analizini sunmaktadır. Araştırma, yedi farklı müzik eserinin MP3 kayıtlarını kullanmaktadır. Bu kayıtlar arasında şunlar bulunmaktadır: aynı parçanın farklı orkestra yorumları, farklı sanatçılar (hem kadın hem erkek şarkıcılar dahil) tarafından seslendirilen performanslar ve aynı eserin bir kadın ve bir erkek sanatçı tarafından seslendirilen versiyonları. Analiz sırasında, her eserin tam kayıtları ve ilk 30 saniyelik bölümleri değerlendirilmiştir. Her ses dosyası için şu analizler gerçekleştirilmiştir: dalga formu görselleştirme, spektrogram, güç spektral yoğunluğu (PSD) ve Mel-Frekans Kepstral Katsayıları (MFCC'ler). Hesaplanan bilgi teorisi metrikleri şunlardır: Shannon entropisi, perplexity, Kullback-Leibler (KL) diverjansı, ortak entropi, çapraz entropi, koşullu entropi ve karşılıklı bilgi. Ek olarak, MFCC'ler ve spektral merkez gibi ses özellikleri için t-testi p-değerleri elde edildi. Grup içi ve gruplar arası karşılaştırmalar yoluyla, orkestra, sanatçı ve cinsiyet farklılıklarının bu metrikler üzerindeki etkileri araştırıldı. Sonuçlar, müzikal yorumlamadaki farklılıkların hem bilgi teorisi metrikleri hem de ses özellikleri üzerinde istatistiksel olarak ölçülebilir etkiler yarattığını göstermektedir.

Anahtar Kelimeler

• Karşılaştırmalı Müzik Analizi
• Bilgi Teorisi Ses
• MFCC Özellikleri
• Performans Değişkenliği
• Dijital Müzik Kayıtları

Comparative Analysis of Different Performances in Digital Music Recordings Using Information Theory Metrics and Audio Features

Öz

This study presents a comparative analysis of different performances of the same musical work using information theory metrics and audio features. The research utilizes MP3 recordings of seven distinct musical works. These recordings include: different orchestral interpretations of the same piece, vocal performances by different artists (including both female and male singers), and versions of the same work performed by one female and one male artist. During the analysis, both the full recordings and the initial 30-second segments of each work were evaluated. For each audio file, the following analyses were performed: waveform visualization, spectrogram, power spectral density (PSD), and Mel-Frequency Cepstral Coefficients (MFCCs). Information theory metrics calculated include: Shannon entropy, perplexity, Kullback-Leibler (KL) divergence, joint entropy, cross entropy, conditional entropy, and mutual information. Additionally, t-test p-values for audio features such as MFCCs and spectral centroid were obtained. Through within-group and between-group comparisons, the effects of orchestra, artist, and gender differences on these metrics were investigated. The results demonstrate that differences in musical interpretation create statistically measurable effects on both information theory metrics and audio features.

Anahtar Kelimeler

• Comparative Music Analysis
• Information Theory Audio
• MFCC Features
• Performance Variability
• Digital Music Recordings

Kaynakça

1. Zhang, M. (2025). Advancing deep learning for expressive music composition and performance modeling. Scientific Reports, 15(1), 28007. https://doi.org/10.1038/s41598-025-13064-6.
2. Nasr, M. A., El-Shafai, W., Abdel-Salam, N., ElRabaie, E.-S. M., El-Fishawy, A. S., & El-Samie, F. E. A. (2025). A comprehensive survey of audio forgery detection: challenges and novel trends. Journal of Electrical Systems and Information Technology, 12(1), 30. https://doi.org/10.1186/s43067-025-00225-w.
3. Chyrun, L., Vysotska, V., Tchynetskyi, S., Ushenko, Y., & Uhryn, D. (2024). Information technology for sound analysis and recognition in the metropolis based on machine learning methods. International Journal of Intelligent Systems and Applications, 16(6), 40–72. https://doi.org/10.5815/ijisa.2024.06.03.
4. MacPhail, A. G., Yip, D. A., Knight, E. C., Hedley, R., Knaggs, M., Shonfield, J., UphamMills, E., & Bayne, E. M. (2024). Audio data compression affects acoustic indices and reduces detections of birds by human listening and automated recognisers. Bioacoustics, 33(1), 74– 90. https://doi.org/10.1080/09524622.2023.2290718.
5. Shapovalova, L., Chernoivanenko, A., Bondar, I., Filatova, O., & Murza, S. (2024). Genre and species characteristics of musical and performing interpretation. Convergences - Journal of Research and Arts Education, 17(33), 161–176. https://doi.org/10.53681/c1514225187514391s.3 3.241.
6. Smith, J. B. L., Chuan, C.-H., & Chew, E. (2014). Audio properties of perceived boundaries in music. IEEE Transactions on Multimedia, 16(5), 1219–1228. https://doi.org/10.1109/TMM.2014.2310706.
7. Foster, P., Dixon, S., & Klapuri, A. (2015). Identifying cover songs using information theoretic measures of similarity. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 23(6), 993–1005. https://doi.org/10.1109/TASLP.2015.2416655.
8. Lee, J., & Lee, J.-S. (2018). Music popularity: Metrics, characteristics, and audio-based prediction. IEEE Transactions on Multimedia, 20(11), 3173–3182. https://doi.org/10.1109/TMM.2018.2820903.

9. Gurjar, K., & Moon, Y. S. (2018). A comparative analysis of music similarity measures in music information retrieval systems. Journal of Information Processing Systems, 14(1), 32–55. https://doi.org/10.3745/JIPS.04.0054.
10. Guerrero, D., Rivera, P., Febres, G., & Gershenson, C. (2021). Towards a measure for characterizing the informational content of audio signals and the relation between complexity and auditory encoding. Entropy, 23(12), 1613. https://doi.org/10.3390/e23121613.
11. Chakrabarty, S., Islam, R., Pricop, E., & Sarma, H. K. D. (2022). An approach to discover similar musical patterns. IEEE Access, 10, 47322–47339. https://doi.org/10.1109/ACCESS.2022.3169362.
12. Flieder, D. (2025). Towards a mathematical foundation for music theory and composition: a theory of structure. Journal of Mathematics and Music, 19(1), 1–27. https://doi.org/10.1080/17459737.2024.2379788.
13. Gourisaria, M. K., Agrawal, R., Sahni, M., & Singh, P. K. (2024). Comparative analysis of audio classification with MFCC and STFT features using machine learning techniques. Discover Internet of Things, 4(1), 1. https://doi.org/10.1007/s43926-023-00049-y.
14. Lu, C. (2025). A semantic generalization of Shannon’s information theory and applications. Entropy, 27(5), 461. https://doi.org/10.3390/e27050461.
15. Nicholson, S., & Kim, E. (2016). Structures in Sound: Analysis of classical music using the information length. Entropy, 18(7), 258. https://doi.org/10.3390/e18070258.
16. Chaudhary, S., Kakarwal, S., & Deshmukh, R. (2021). Musical instrument recognition using audio features with integrated entropy method. Journal of Integrated Science and Technology, 9(2), 92–97.
17. Moysis, L., Karasavvidis, K., Kampelopoulos, D., Boursianis, A. D., Sotiroudis, S., Nikolaidis, S., Volos, C., Sarigiannidis, P., Matin, M. A., & Goudos, S. K. (2025). Identification of Greek Orthodox church chants using fuzzy entropy. Computers, 14(2), 39. https://doi.org/10.3390/computers14020039.
18. Constantinescu, C., & Brad, R. (2023). An overview on sound features in time and frequency domain. International Journal of Advanced Statistics and IT&C for Economics and Life Sciences, 13(1), 45–58. https://doi.org/10.2478/ijasitels-2023-0006.
19. Ayushi, Khurana, S., & Bansal, P. (2025). Emotion detection using multimodal fusion of acoustic features for robust audio representation. In Lecture Notes in Networks and Systems: 1233 LNNS (pp. 199–211). Springer, Singapore. https://doi.org/10.1007/978-981-96-3287-9_17.
20. Youngworth, R. N., Gallagher, B. B., & Stamper, B. L. (2005). An overview of power spectral density (PSD) calculations. In H. P. Stahl (Ed.), https://doi.org/10.1117/12.618478 (Vol. 5869, p. 58690U). SPIE. https://doi.org/10.1117/12.618478.
21. Kua, J. M. K., Thiruvaran, T., Nosratighods, M., Ambikairajah, E., & Epps, J. (2010). Investigation of spectral centroid magnitude and frequency for speaker recognition. Proceedings Odyssey 2010 The Speaker and Language Recognition Workshop, 34–39.
22. Bromiley, P., Thacker, N., & Bouhova-Thacker, E. (2004). Shannon entropy, Renyi entropy, and information.
23. Miaschi, A., Brunato, D., Dell’Orletta, F., & Venturi, G. (2021). What makes my model perplexed? A linguistic investigation on neural language models perplexity. Proceedings of Deep Learning Inside Out (DeeLIO): The 2nd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures, 40– 47. https://doi.org/10.18653/v1/2021.deelio-1.5.
24. Ma, Y.-J., Wang, F., Wu, X.-Y., & Cai, K.-Y. (2025). Research on the characteristics of joint distribution based on minimum entropy. Mathematics, 13(6), 972. https://doi.org/10.3390/math13060972.
25. Hancer, E., Xue, B., & Zhang, M. (2025). A survey on evolutionary feature selection in multilabel classification. IEEE Transactions on Evolutionary Computation, 1–1. https://doi.org/10.1109/TEVC.2025.3544681.
26. Rafflesia Chackochan, M., Sankaran, P. G., & Unnikrishnan Nair, N. (2025). Bivariate Kullback–Leibler divergence. Communications in Statistics - Theory and Methods, 1–21. https://doi.org/10.1080/03610926.2025.2496687.
27. Hosseini, S., & Sadeghkhani, I. (2025). Crossentropy based AC series arc fault detection for more electric aircrafts. Scientific Reports, 15(1), 26108. https://doi.org/10.1038/s41598-025- 11935-6.
28. Rahimi, M., & Bidabadi, N. (2025). A formula of conditional entropy for metrics induced by probability bi-sequences. Acta Mathematica Scientia, 45(4), 1619–1639. https://doi.org/10.1007/s10473-025-0420-3.
29. López De Luise, D. (2021). Language and reasoning by entropy fractals. Signals, 2(4), 754– 770. https://doi.org/10.3390/signals2040044.
30. Manzo-Martínez, A., Gaxiola, F., RamírezAlonso, G., & Martínez-Reyes, F. (2022). A comparative study in machine learning and audio features for kitchen sounds recognition. Computación y Sistemas, 26(2), 603–621. https://doi.org/10.13053/cys-26-2-4244.