基于音频内容分析和机器学习技术的哥伦比亚传统音乐类型自动识别
Automatic Identification of Traditional Colombian Music Genres based on Audio Content Analysis and Machine Learning Technique
摘要：
哥伦比亚在传统音乐中有多种流派，可以根据地区表达哥伦比亚文化的丰富性。这种音乐的多样性是非洲，本土土著和欧洲影响的混合物的结果。组织大量的歌曲集合是一项耗时的任务，需要人类收听音频片段以识别流派、歌手、年份、乐器和允许索引歌曲数据集的其他相关特征。本文提出了一种方法来自动识别类型的哥伦比亚歌曲，通过其音频内容。该方法提取用于训练学习分类流派的机器学习模型的音频特征。该方法进行了评估，在数据集的180个音乐作品属于6个民间哥伦比亚音乐流派：Bambuco，Cecula，Cumbia，Joropo，Pasillo，和Vallenato。结果表明，尽管哥伦比亚节奏的复杂性达到69%的平均准确率，但可以自动识别音乐流派。
英文摘要：
Colombia has a diversity of genres in traditional music, which allows to express the richness of the Colombian culture according to the region. This musical diversity is the result of a mixture of African, native Indigenous, and European influences. Organizing large collections of songs is a time consuming task that requires that a human listens to fragments of audio to identify genre, singer, year, instruments and other relevant characteristics that allow to index the song dataset. This paper presents a method to automatically identify the genre of a Colombian song by means of its audio content. The method extracts audio features that are used to train a machine learning model that learns to classify the genre. The method was evaluated in a dataset of 180 musical pieces belonging to six folkloric Colombian music genres: Bambuco, Carranga, Cumbia, Joropo, Pasillo, and Vallenato. Results show that it is possible to automatically identify the music genre in spite of the complexity of Colombian rhythms reaching an average accuracy of 69\%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
