Explainable Modeling of Gender-Targeting Practices in Toy Advertising Sound and Music

This study examines gender coding in sound and music, in a context where music plays a supportive role to other modalities, such as in toy advertising. We trained a series of binary XGBoost classifiers on handcrafted features extracted from the soundtracks and then performed SAGE and SHAP analyses to identify key audio features in predicting the gender target of the ads. Our analysis reveals that timbral dimensions play a prominent role and that commercials aimed at girls tend to be more harmonious and rhythmical, with a broader and smoother spectrum, while those targeting boys are characterised by higher loudness, spectral entropy, and roughness. Mixed audience commercials instead appear to be as rhythmical as girls-only ads, although slower, but show intermediate characteristics in terms of harmonicity and roughness. This study highlights the importance of music in shaping societal norms and the need for greater accountability in its use in marketing and other industries. We provide a public repository containing all code and data used in this study.