We introduce a novel audio corpus, the FAV Corpus, of over 400 favorite musical excerpts and pieces, formal analyses, and free-response comments. In a survey, 140 American university students (mostly music majors) were asked to provide three of their favorite 15-second musical excerpts, from any genre or time period. For each selection, respondents were asked: “Why do you love the excerpt? Try to be as specific and detailed as possible (music theory terms are encouraged but not required).” Classical selections were dominated by a very small number of composers, while the pop and jazz artists were diverse. A thematic coding of the respondents’ comments found that the most common themes were melody (34.2% of comments), harmony (27.2%), and sonic factors: texture (27.6%), instrumentation (24.3%), and timbre (12.5%). (Rhythm (19.5%) and meter (4.6%) were less present in the comments.) The comments cite simplicity three times more than complexity, and energy gain 14 times more than energy decrease, suggesting that people's favorite excerpts involve simple moments of energy gain or "build-up". The complete FAV Corpus is publicly available online at EthanLustig.com/FavCorpus. We will discuss future possibilities for the corpus, including potential directions in the spaces of machine learning and music recommendation.

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