Paper year
2025
Paper dossier
TuFoC: regional classification of Turkish folk music recordings using deep learning on Mel spectrograms
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unknown
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Controlled edge slice
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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
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Signals: semantic=0.8617, 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: 0.1723)
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
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Similarity penaltypenalty
Similarity penalty: reduces score when non-zero (contribution to score: 0.0000)
Bridge
Present in run, outside top 50
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)
Under-cited
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Abstract
The regional classification of Turkish folk music remains a relatively unexplored domain in music information retrieval, particularly when leveraging raw audio signals for deep learning. This study addresses this gap by investigating how modern deep learning architectures can effectively classify regional folk recordings from limited original data using carefully designed spectrogram inputs. To address this challenge, we present Turkish Folk Music Classification (TuFoC), a deep learning-based approach that classifies Turkish folk music recordings according to their region of origin using Mel spectrogram representations. We investigate two complementary architectures: a MobileNetV2-based convolutional neural network (CNN) for extracting spatial representations, and a long short-term memory (LSTM) network for modeling temporal dynamics. To engage critically with the state of the art, we replicate a classical machine learning pipeline that combines histogram of oriented gradients (HOG) features with a Light Gradient-Boosting Machine (LightGBM) classifier and Synthetic Minority Oversampling Technique (SMOTE)-based oversampling. This baseline achieves competitive results, particularly on GAD, with 92.53% accuracy and macro F1-score. However, our CNN architecture not only marginally outperforms the baseline on the gently augmented dataset (GAD) (92.68% accuracy) but also demonstrates superior consistency across all dataset variants. While the LSTM model underperforms on original dataset (OD) and strongly augmented dataset (AD), it improves markedly on GAD, validating the importance of balanced data preparation. Experimental results, obtained through stratified 10-fold cross-validation repeated over 30 independent runs, demonstrate that the proposed CNN architecture delivers the highest and most stable classification performance. Beyond performance gains, our approach offers key advantages in generalization, scalability, and automation, as it bypasses the need for domain-specific feature design. The findings confirm that deep neural models trained on Mel spectrograms constitute a flexible and robust alternative to classical pipelines, holding promise for future applications in computational ethnomusicology and music information retrieval.
Authors
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