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.