Effects of Album and Artist Filters in Audio Similarity Computed for Very Large Music Databases
Effects of Album and Artist Filters in Audio Similarity Computed for Very Large Music Databases
复制标题
专辑和艺术家过滤器对大型音乐数据库计算的音频相似度的影响
DOI：
--
复制
发表时间：
2010
影响因子：
--
通讯作者：
Dominik Schnitzer
中科院分区：
文献类型：
--
作者：
A. Flexer;Dominik Schnitzer
在音乐信息检索中，一个核心目标是根据查询的歌曲或歌手自动向用户推荐音乐。这可以使用专家知识（例如www.pandora.com）、社会元数据（例如www.last.fm）、协同过滤（例如www.amazon.com/mp3）或直接从音频中提取信息（例如www.muffin.com）来完成。在基于音频的音乐推荐中，一个众所周知的效应是推荐列表中与查询歌曲来自同一艺术家的歌曲占主导地位。这种效应主要是在体裁分类实验的背景下研究的。由于音乐相似度通常不存在基本真理，因此流派分类被广泛用于音乐相似度的评价。每首歌都被标记为属于一个音乐流派，例如使用音乐专家的建议。较高的体裁分类结果表明相似性度量较好。如果在类型分类实验中，来自同一艺术家的歌曲在训练集和测试集都是允许的，这可能会导致过于乐观的结果，因为通常来自一个艺术家的所有歌曲都有相同的类型标签。可以说，在这种情况下，人们是在进行艺术家分类，而不是类型分类。人们甚至可以推测，专辑的特定声音（母带和制作效果）正在被分类。在Pampalk， Flexer和Widmer（2005）中，提出了使用所谓的“艺术家过滤器”来确保给定艺术家的歌曲要么全部在训练集中，要么全部在测试集中。这些作者发现，使用这种艺术家滤镜可以降低
In music information retrieval, one of the central goals is to automatically recommend music to users based on a query song or query artist. This can be done using expert knowledge (e.g., www.pandora.com), social meta-data (e.g., www.last.fm), collaborative filtering (e.g., www.amazon.com/mp3), or by extracting information directly from the audio (e.g., www.muffin.com). In audio-based music recommendation, a wellknown effect is the dominance of songs from the same artist as the query song in recommendation lists. This effect has been studied mainly in the context of genre-classification experiments. Because no ground truth with respect to music similarity usually exists, genre classification is widely used for evaluation of music similarity. Each song is labelled as belonging to a music genre using, e.g., advice of a music expert. High genre classification results indicate good similarity measures. If, in genre classification experiments, songs from the same artist are allowed in both training and test sets, this can lead to over-optimistic results since usually all songs from an artist have the same genre label. It can be argued that in such a scenario one is doing artist classification rather than genre classification. One could even speculate that the specific sound of an album (mastering and production effects) is being classified. In Pampalk, Flexer, and Widmer (2005) the use of a so-called “artist filter” that ensures that a given artist’s songs are either all in the training set, or all in the test set, is proposed. Those authors found that the use of such an artist filter can lower the