کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4948631 1439619 2016 9 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Bridge the semantic gap between pop music acoustic feature and emotion: Build an interpretable model
ترجمه فارسی عنوان
فاصله ی معنایی بین ویژگی های صوتی موسیقی پاپ و احساسات را بسازید: یک مدل تفسیری ایجاد کنید
کلمات کلیدی
شکاف معنایی، احساسات موسیقی، انتخاب ویژگی، روش های انقباض، مدل قابل ترجمه
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی
Music emotion recognition (MER) is an important topic in music understanding, recommendation, retrieval and human computer interaction. Great success has been achieved by machine learning methods in estimating human emotional response to music. However, few of them pay much attention in semantic interpret for emotion response. In our work, we first train an interpretable model between acoustic audio and emotion. Filter, wrapper and shrinkage methods are applied to select important features. We then apply statistical models to build and explain the emotion model. Extensive experimental results reveal that the shrinkage methods outperform the wrapper methods and the filter methods in arousal emotion. In addition, we observed that only a small set of the extracted features have the key effects to arousal. While, most of our extracted features have small contribution to valence music perception. Ultimately, we obtain a higher average accuracy rate in arousal, compared to that in valence.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 208, 5 October 2016, Pages 333-341
نویسندگان
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