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

Abstract

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.

Keywords

• 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

Abstract

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.

Keywords

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

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