You Don’t Have to Know it to Like it: General Familiarity as a Predictor of Liking for Music Chosen by the Armonique Music Search Engine

Faculty Supervisor:

Dwight Krehbiel

Year of Project Completion:

2009

Student:

Sierra Pryce & Tim Burns

Abstract

Abstract of a poster presented at the Student Research Conference of Sigma Xi, The Woodlands, TX, Nov. 14,2009: Previous research indicates familiarity is a strong predictor of liking, but does a listener have to be familiar with a song to like it? A listener may be familiar with a genre of music but still have no specific familiarity with many examples from that genre. We hypothesized that specific familiarity may not be necessary for liking. We examined this question in a study to test liking of music chosen by a search engine called Armonique. Twenty-five participants indicated their three favorite genres of music out of a list of eight available. Experimenters chose two original pieces among these preferred genres. The search engine chose two similar and two dissimilar pieces for each original, based on the features of timbre and melody, resulting in two unique sets of five pieces. Participants listened to their two sets, and rated each one minute piece for liking, genre familiarity, and specific familiarity. Significant differences in participant liking ratings were seen between the original and the dissimilar pieces (higher for the original in paired t-tests, p < 0.01 ), but not between the original and similar pieces. Therefore, Armonique is capable of choosing pieces that users will like, based on a preferred genre. Specific familiarity was uniformly low across all songs and, hence, was not a predictor of liking. Nonetheless, hierarchical linear models showed that general familiarity was a strong predictor of liking in both sets of music (p < 0.001 ). These results confirm the relationship of general familiarity and liking, even in the absence of specific familiarity.