A Game-Based Approach for Collecting Semantic Annotations of Music

Games based on human computation are a valuable tool for collecting semantic information about images. We show how to transfer this idea into the music domain in order to collect high-quality semantic information about songs. We present Listen Game, a online, multiplayer game that measures the semantic relationship between music and words. In the normal mode, a player sees a list of semantically related words (e.g., instruments, emotions, usages, genres) and is asked to pick the best and worst word to describe a song. In the freestyle mode, a user is asked to suggest a new word that describes the music. Each player receives realtime feedback about the agreement amongst all players. We show that we can use the data collected during a two-week pilot study of Listen Game to learn a supervised multiclass labeling (SML) model. We show that this SML model can annotate a novel song with meaningful words and retrieve relevant songs from a database of audio content.