Paper year
2026
Paper dossier
A Multidimensional MIR Analysis of Acoustic, Linguistic and Cultural Gaps Between Maskandi and Western 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
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Signals: semantic=0.8679, 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: 0.1736)
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
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
Contemporary Music Information Retrieval (MIR) and Natural Language Processing (NLP) systems are increasingly applied to diverse musical traditions, yet they are largely grounded in Western musical and linguistic assumptions. This study examines whether commonly used MIR features and multilingual NLP models adequately represent the acoustic, linguistic, and cultural structures of Maskandi music in comparison to Western music and identifies where representational gaps and biases arise. A multidimensional framework was employed, comprising acoustic and structural MIR analysis, linguistic and semantic lyrical analysis, and bias analysis. A curated dataset of 60 recordings and corresponding lyrics was analysed using rhythm and beat features, pitch contour measures, structural self-similarity, timbre embeddings, semantic similarity, lexical diversity, metaphor density, topic modelling, multilingual embeddings, and dataset-level audits. The results reveal systematic representational failures: beat tracking showed lower median IOI coefficient of variation for Maskandi (0.028) versus Western music (0.040, p = 0.0199) yet exhibited greater algorithmic instability, tempo averaged 131.16 BPM versus 111.69 BPM (p = 0.000262), pitch glide proportions were significantly higher in Maskandi (0.34 vs. 0.16), on-beat energy ratios differed substantially (2.26 vs. 1.19, p < 0.0000007), semantic similarity revealed high intra-genre coherence for Maskandi (0.73) versus Western (0.25), metaphor density approached zero in Maskandi versus up to 7 per 100 words in Western lyrics, topic modeling produced two compact clusters for Maskandi versus 6 dispersed clusters for Western, timbre embeddings achieved a 0.405 silhouette score, dataset audits revealed 0% Maskandi representation across seven major MIR corpora with African traditions comprising <3%. The study concludes that statistical separability does not imply representational adequacy and highlights the need for culturally grounded MIR and NLP representations to support diverse musical traditions.
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