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With digital music consumption being at an all-time high, online music encyclopedia like MusicBrainz and music intelligence platforms like The Echo Nest are becoming increasingly important in identifying, organizing, and recommending music for listeners around the globe. As a byproduct, such sites collect comprehensive information about a vast amount of artists, their recorded songs, institutional support, and the collaborations between them. Using a unique mash-up of crowdsourced, curated, and algorithmically augmented data, this paper unpacks an unsolved problem that is key to promoting artistic innovation, i.e., how gender penetrates into artistic context leading to the globally perceived gender gap in the music industry. Specifically, we investigate gender-related differences in the sonic features of artists’ work, artists’ tagging by listeners, their record label affiliations, and collaboration networks. We find statistically significant disparities along all these dimensions. Moreover, the differences allow models to reliably identify the gender of songs’ creators and help elucidate the role of cultural and structural factors in sustaining inequality. Our findings contribute to a better understanding of gender differences in music production and inspire strategies that could improve the recognition of female artists and advance gender equity in artistic leadership and innovation.
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Exploring Acoustic Similarity and Preference for Novel Music Recommendation
Most commercial music services rely on collaborative filtering to recommend artists and songs. While this method is effective for popular artists with large fanbases, it can present difficulties for recommending novel, lesser known artists due to a relative lack of user preference data. In this paper, we therefore seek to understand how content-based approaches can be used to more effectively recommend songs from these lesser known artists. Specifically, we conduct a user study to answer three questions. Firstly, do most users agree which songs are most acoustically similar? Secondly, is acoustic similarity a good proxy for how an individual might construct a playlist or recommend music to a friend? Thirdly, if so, can we find acoustic features that are related to human judgments of acoustic similarity? To answer these questions, our study asked 117 test subjects to compare two unknown candidate songs relative to a third known reference song. Our findings show that 1) judgments about acoustic similarity are fairly consistent, 2) acoustic similarity is highly correlated with playlist selection and recommendation, but not necessarily personal preference, and 3) we identify a subset of acoustic features from the Spotify Web API that is particularly predictive of human similarity judgments.
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- Award ID(s):
- 1901330
- PAR ID:
- 10290593
- Date Published:
- Journal Name:
- International Symposium on Music Information Retrieval
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
