We investigate listener preference in multitrack music production using the Mix Evaluation Dataset, comprised of 184 mixes across 19 songs. Features are extracted from verses and choruses of stereo mixdowns. Each observation is associated with an average listener preference rating and standard deviation of preference ratings. Principal component analysis is performed to analyze how mixes vary within the feature space. We demonstrate that virtually no correlation is found between the embedded features and either average preference or standard deviation of preference. We instead propose using principal component projections as a semantic embedding space by associating each observation with listener comments from the Mix Evaluation Dataset. Initial results disagree with simple descriptions such as "width" or "loudness" for principal component axes.
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机译：我们使用Mix评估数据集调查MultiTragr Music Prodations中的侦听器偏好，其中包括19首歌曲的184个混合。从立体声混合的经文和合唱中提取特征。每个观察与均方听众偏好额定值和偏好额定值的标准偏差相关联。执行主成分分析以分析混合在特征空间内的变化。我们证明，在嵌入功能和偏好的平均偏好或标准偏差之间，实际上没有发现相关性。当我们将每个观察与来自MIX评估数据集中的侦听器注释相关联时，我们将主成分投影用作语义嵌入空间。初始结果不同意主组件轴的简单描述，例如“宽度”或“响度”。
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