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grittiness    
n. 有砂砾,坚韧不拔

有砂砾,坚韧不拔


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英文字典中文字典相关资料:


  • Responsible Music Genre Classification Using Interpretable . . .
    We introduce an approach that leverages both traditional machine learning (ML) techniques and modern deep learning approaches, including convolutional neural networks (CNNs) and vision transformers (ViTs), to enhance the accuracy and efficiency of music genre classification
  • Harmonizing minds and machines: survey on transformative . . .
    We provide a comprehensive survey of the application of ML in music, covering key areas like music recommendation, music classification, music transcription, and music generation We analyze the evolution of methodologies in these areas, from basic rule-based systems to sophisticated ML models
  • Music Genre Classification: A Comprehensive Study on Feature . . .
    Specifically, we trained a model that can tag and boost based on the musical genre of the corresponding track efficiency in the classification, organization and management of music
  • Robustness of musical features on deep learning models for . . .
    MFCC and Swaragram musical features capture enough latent patterns of underlying musical genre to perform exceptionally across datasets and selected models This shows that the design of the MFCC feature to capture the human auditory system is highly applicable in music genre classification
  • Deformer: Denoising Transformer for Improved Audio Music . . .
    In this paper, a novel method for audio music genre classification is proposed The proposed method is characterized by denoising, which not only reduces computational costs compared to MoCo-based strategies but also offers a robust performance that is uninhibited by hyperparameter dependency
  • Music Genre Classification Using Machine Learning (XGBoost . . .
    This paper reviews recent studies in music genre classification, focusing on the effectiveness of XGBoost and other machine learning algorithms Despite challenges like limited datasets and the absence of lyrical data, XGBoost has shown promising results
  • Bridging Interpretability and Robustness Using LIME-Guided . . .
    In this paper, we propose a novel framework that leverages LIME to systematically enhance the robustness of CNNs Unlike prior approaches that treat interpretability as a passive tool for post-hoc analysis, we employ LIME as an active guide for model refinement





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