A deep matrix factorization method for learning attribute representations

Semi-Non-negative Matrix Factorization is a technique that learns a low-dimensional representation of a dataset that lends itself to a clustering interpretation. It is possible that the mapping between this new representation and our original data matrix contains rather complex hierarchical information with implicit lower-level hidden attributes, that classical one level clustering methodologies can not interpret. In this work we propose a novel model, Deep Semi-NMF, that is able to learn such hidden representations that allow themselves to an interpretation of clustering according to different, unknown attributes of a given dataset. We also present a semi-supervised version of the algorithm, named Deep WSF, that allows the use of (partial) prior information for each of the known attributes of a dataset, that allows the model to be used on datasets with mixed attribute knowledge. Finally, we show that our models are able to learn low-dimensional representations that are better suited for clustering, but also classification, outperforming Semi-Non-negative Matrix Factorization, but also other state-of-the-art methodologies variants.Comment: Submitted to TPAMI (16-Mar-2015

Gendering the Virtual Space: Sonic Femininities and Masculinities in Contemporary Top 40 Music

This dissertation analyzes vocal placement—the apparent location of a voice in the virtual space created by a recording—and its relationship to gender. When listening to a piece of recorded music through headphones or stereo speakers, one hears various sound sources as though they were located in a virtual space (Clarke 2013). For instance, a specific vocal performance—once manipulated by various technologies in a recording studio—might evoke a concert hall, an intimate setting, or an otherworldly space. The placement of the voice within this space is one of the central musical parameters through which listeners ascribe cultural meanings to popular music. I develop an original methodology for analyzing vocal placement in recorded popular music. Combining close listening with music information retrieval tools, I precisely locate a voice’s placement in virtual space according to five parameters: (1) Width, (2) Pitch Height, (3) Prominence, (4) Environment, and (5) Layering. I use the methodology to conduct close and distant readings of vocal placement in twenty-first-century Anglo-American popular music. First, an analysis of “Love the Way You Lie” (2010), by Eminem feat. Rihanna, showcases how the methodology can be used to support close readings of individual songs. Through my analysis, I suggest that Rihanna’s wide vocal placement evokes a nexus of conflicting emotions in the wake of domestic violence. Eminem’s narrow placement, conversely, expresses anger, frustration, and violence. Second, I use the analytical methodology to conduct a larger-scale study of vocal placement in a corpus of 113 post-2008 Billboard chart-topping collaborations between two or more artists. By stepping away from close readings of individual songs, I show how gender stereotypes are engineered en masse in the popular music industry. I show that women artists are generally assigned vocal placements that are wider, more layered, and more reverberated than those of men. This vocal placement configuration—exemplified in “Love the Way You Lie”—creates a sonic contrast that presents women’s voices as ornamental and diffuse, and men’s voices as direct and relatable. I argue that these contrasting vocal placements sonically construct a gender binary, exemplifying one of the ways in which dichotomous conceptions of gender are reinforced through the sound of popular music