Exploring Diverse Sounds: Identifying Outliers in a Music Corpus
<|reference_start|>Exploring Diverse Sounds: Identifying Outliers in a Music Corpus: Existing research on music recommendation systems primarily focuses on recommending similar music, thereby often neglecting diverse and distinctive musical recordings. Musical outliers can provide valuable insights due to the inherent diversity of music itself. In this paper, we explore music outliers, investigating their potential usefulness for music discovery and recommendation systems. We argue that not all outliers should be treated as noise, as they can offer interesting perspectives and contribute to a richer understanding of an artist's work. We introduce the concept of 'Genuine' music outliers and provide a definition for them. These genuine outliers can reveal unique aspects of an artist's repertoire and hold the potential to enhance music discovery by exposing listeners to novel and diverse musical experiences.<|reference_end|>
@article{cai2024exploring,
    title={Exploring Diverse Sounds: Identifying Outliers in a Music Corpus},
    author={Le Cai, Sam Ferguson, Gengfa Fang, Hani Alshamrani},
    journal={The 16th International Symposium on Computer Music
  Multidisciplinary Research,2023},
    year={2024},
    doi={10.5281/zenodo.10114235},
    archivePrefix={arXiv},
    eprint={2404.06103},
    primaryClass={cs.SD cs.IR eess.AS}
}