音声スペクトログラムから音楽ジャンル分類のためのsqueezenetとプロモートされた理想気体の分子運動を使用するディープラーニングモデル
1Department of Cultural and Creative Arts, The Education University of Hong Kong, 10 Lo Ping Road, Tai Po, New Territories, Hong Kong. xuemengjin2025@163.com.
Scientific reports
|September 1, 2025
まとめ
この研究は,正確な音楽ジャンルの分類のために,Promoted Ideal Gas Molecular Motion (PIGMM) で最適化された新しいSqueezeNetモデルを導入しています. ハイブリッドアプローチは高精度で 既存の方法よりも性能が優れており 軽量で騒音に耐えるソリューションです
科学分野:
- コンピュータ科学
- 人工知能
- 音楽情報検索
背景:
- 音楽のジャンル分類 (MGC) は,高次元,変数,騒々しい音声信号のために決定的ですが,挑戦的です.
- 伝統的なディープラーニングモデルは,しばしばオーバーフィットとローカル・オプティマと闘い,かなりの計算リソースを必要とします.
研究 の 目的:
- 新しいハイブリッドモデルを提案し,音楽のジャンル分類のパフォーマンスを向上させる.
- 複雑なオーディオデータの処理における伝統的なディープラーニングモデルの限界を解決する.
主な方法:
- SqueezeNetモデルは,カオス理論と対立ベースの学習を組み込んだメタヒューリスティックアルゴリズムであるPromoted Ideal Gas Molecular Motion (PIGMM) を使用して最適化されました.
- モデルは,GTZANとExtended Ballroomのデータセットで10倍クロス検証を使用して,オーディオスペクトログラムで訓練され,検証されました.
主要な成果:
- 提案されたモデルは特徴抽出において96%の精度を達成した.
- 最先端のモデルを上回る91.1% (GTZAN) と93.4% (Extended Ballroom) の分類精度が得られた.
- 高精度 (最大95.8%) とリコール (最大97.7%) の値は,モデルの有効性を確認しています.
結論:
- SqueezeNet-PIGMMハイブリッドモデルは,スケーラブルな音楽ジャンル分類のための軽量でノイズに耐えるソリューションを提供します.
- このアプローチは,既存の方法と比較して優れた性能と汎用性を示しています.
- この研究は,音声信号処理におけるディープラーニングモデルの改善のためのメタヒューリスティック最適化の可能性を強調しています.
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