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Research on Architecture for Long-tailed Genre Computer Intelligent Classification with Music Information Retrieval and Deep Learning paper by Independent / Community
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Paper

Research on Architecture for Long-tailed Genre Computer Intelligent Classification with Music Information Retrieval and Deep Learning

by Independent / Community 008969ab4c4a509d7863b33fe7fe351793d11639
Free2AITools Nexus Index
62.3
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 70
P: Popularity 45
R: Recency 100
Q: Quality 65
Tech Context
Vital Performance —

In this paper, we propose a Musical Attention Network (MAN) architecture for long-tailed, imbalanced music genre classification which is often ignored and quite prevalent in Music Information Retrieval (MIR). Here, the challenge is to classify the genre of long-tailed music accurately. Inspired by the recent progress in NLP, the proposed model can take advantage of genre correlations to better identify informative segments. Comprehensive experimental results demonstrate our model brings signi...

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Semantic Scholar 4 Citations
Paper Information Summary
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Registry ID 008969ab4c4a509d7863b33fe7fe351793d11639
License ArXiv
Provider semantic_scholar
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Cite this paper

Academic & Research Attribution

BibTeX 
@misc{008969ab4c4a509d7863b33fe7fe351793d11639,
  author = {Unknown},
  title = {Research on Architecture for Long-tailed Genre Computer Intelligent Classification with Music Information Retrieval and Deep Learning Paper},
  year = {2026},
  howpublished = {\url{https://api.semanticscholar.org/008969ab4c4a509d7863b33fe7fe351793d11639}},
  note = {Accessed via Free2AITools.}
}
APA Style
Unknown. (2026). Research on Architecture for Long-tailed Genre Computer Intelligent Classification with Music Information Retrieval and Deep Learning [Paper]. Free2AITools. https://api.semanticscholar.org/008969ab4c4a509d7863b33fe7fe351793d11639

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βš–οΈ Free2AITools Nexus Index V2.0

Methodology How FNI works
Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 70
Popularity (P) 45
Recency (R) 100
Quality (Q) 65

πŸ’¬ Index Insight

FNI V2.0 for Research on Architecture for Long-tailed Genre Computer Intelligent Classification with Music Information Retrieval and Deep Learning: Authority (A:70), Popularity (P:45), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

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πŸ“ Executive Summary

"In this paper, we propose a Musical Attention Network (MAN) architecture for long-tailed, imbalanced music genre classification which is often ignored and quite prevalent in Music Information Retrieval (MIR). Here, the challenge is to classify the genre of long-tailed music accurately. Inspired by the recent progress in NLP, the proposed model can take advantage of genre correlations to better identify informative segments. Comprehensive experimental results demonstrate our model brings signi..."

❝ Cite Node 

@article{Unknown2026Research,
  title={Research on Architecture for Long-tailed Genre Computer Intelligent Classification with Music Information Retrieval and Deep Learning},
  author={},
  note={Indexed by Free2AITools},
  year={2026}
}

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πŸ“Š Research Signals

πŸ“ˆ4CitationsSemantic Scholar
πŸ›οΈ70AuthorityFNI pillar
⏱️100RecencyFNI pillar
βœ…65QualityFNI pillar
πŸ—‚οΈknowledge retrievalField

🏷️ Research Topics

attention mechanismrag retrieval
πŸ“¦Data Source: semantic_scholar
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πŸ†” Identity & Source

source
semantic_scholar
author
Unknown
license
ArXiv
tags
paper, research, academic

βš™οΈ Technical Specs

architecture
null
params billions
null
context length
null
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citations
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