Paper year
2026
Paper dossier
Comparative Analysis of SVM, k-NN and Logistic Regression Methods in Classifying Turkish Music Genres
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unknown
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Run shadow-generalization-product-candidate-ranking-v1Top 50 surfaced
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Families present
2
Top 50
0
Run label
shadow-generalization-product-candidate-ranking-v1
Snapshot
source-snapshot-shadow-generalization-v1-20260521
Scope: family global | run rank-83787b91ef
Emerging
Present in run, outside top 50
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Signals: semantic=0.8183, citation_velocity=0.0000, topic_growth=0.0000, diversity_penalty=0.0000
Why this surfaced | 3 used | 1 penalty | 1 not computed
Embedding slice fit (corpus centroid)used
Embedding slice fit (corpus centroid): high; used in final ranking (contribution to score: 0.1637)
Recent attentionused
Recent attention: low; used in final ranking (contribution to score: 0.0000)
Topic momentumused
Topic momentum: low; used in final ranking (contribution to score: 0.0000)
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Similarity penaltypenalty
Similarity penalty: reduces score when non-zero (contribution to score: 0.0000)
Bridge
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Multi-topic paper in active topics; no cluster_version on this run so bridge_score was not computed.
Signals: citation_velocity=0.0000, topic_growth=0.0000, diversity_penalty=1.0000
Why this surfaced | 2 used | 1 penalty | 2 not computed
Semantic matchnot computed
Semantic match: not computed for this run
Recent attentionused
Recent attention: low; used in final ranking (contribution to score: 0.0000)
Topic momentumused
Topic momentum: low; used in final ranking (contribution to score: 0.0000)
Cross-cluster signalnot computed
Cross-cluster signal: not computed for this run
Topic breadth penaltypenalty
Topic breadth penalty: reduces score when non-zero (contribution to score: -0.2000)
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Abstract
Music genre classification represents a fundamental challenge within the field of Music Information Retrieval (MIR). The analysis of audio signals plays a pivotal role in the process of music genre classification, facilitating the extraction of pertinent information from the frequency-based data of the auditory content. In this study, diverse acoustic characteristics were derived through the utilization of the librosa library, and subsequent classification procedures were executed employing machine learning algorithms. For the purpose of this study, a dataset comprising a total of 600 music files in WAV format was meticulously curated. This dataset encompassed six distinct genres, all rooted in Turkish musical traditions. Subsequently, classification tasks were undertaken using Support Vector Machines (SVM), k-Nearest Neighbors (k-NN), and Logistic Regression algorithms. A series of experiments was conducted, varying the kernel functions and distance metrics employed. The findings of this investigation reveal the highest achieved accuracy rates, which amounted to 71.88% with k-NN, 73.44% with Logistic Regression, and 78.65% with the SVM algorithm. Notably, the SVM algorithm demonstrated superior performance in comparison to all other methodologies explored in this study.
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