标题： 基于音频的内容评估的机器学习框架，使用MFCC、色度、频谱对比和时间特征工程 显示英文标题

标题： Machine Learning Framework for Audio-Based Content Evaluation using MFCC, Chroma, Spectral Contrast, and Temporal Feature Engineering

摘要： 本研究提出了一种机器学习框架，用于评估音频内容之间的相似性并预测情感评分。 我们构建了一个包含YouTube上音乐翻唱音频样本以及原歌曲音频的数据库，并提取了来自用户评论的情感评分，作为内容质量的代理标签。 我们的方法涉及大量的预处理，将音频信号分割为30秒的窗口，并通过梅尔频率倒谱系数（MFCC）、色度、频谱对比度和时间特征提取高维特征表示。 利用这些特征，我们训练回归模型在0-100的尺度上预测情感评分，分别获得了3.420、5.482、2.783和4.212的均方根误差（RMSE）值。 观察到基于绝对差分度量的基线模型有所改进。 这些结果展示了机器学习捕捉音频中情感和相似性的潜力，为媒体分析中的AI应用提供了可调整的框架。 显示英文摘要

摘要： This study presents a machine learning framework for assessing similarity between audio content and predicting sentiment score. We construct a dataset containing audio samples from music covers on YouTube along with the audio of the original song, and sentiment scores derived from user comments, serving as proxy labels for content quality. Our approach involves extensive pre-processing, segmenting audio signals into 30-second windows, and extracting high-dimensional feature representations through Mel-Frequency Cepstral Coefficients (MFCC), Chroma, Spectral Contrast, and Temporal characteristics. Leveraging these features, we train regression models to predict sentiment scores on a 0-100 scale, achieving root mean square error (RMSE) values of 3.420, 5.482, 2.783, and 4.212, respectively. Improvements over a baseline model based on absolute difference metrics are observed. These results demonstrate the potential of machine learning to capture sentiment and similarity in audio, offering an adaptable framework for AI applications in media analysis.