Music retrieval by singing and humming using information fusion

We present that combinatorial fusion analysis (CFA) can improve results in a music information retrieval (MIR) task, specifically querying a database of recorded music by singing, humming, or whistling. Our experiment considers 10 scoring systems, 55 queries, and a database of 310 original artists´ recordings. Through the use of spectral subtraction, we exploit the recording industry´s tradition of placing the lead vocal and other prominent melodic features in the center of a stereo mix. We employ the rank/score function previously defined in other studies of CFA to analyze the behavior of scoring systems, and we use the rank/score variation to quantify the diversity of any two scoring systems. We then observe that successful 2-combinations, i.e. cases where the performance of a combination meets or exceeds the performance of its constituent scoring systems, tend to occur when each system performs relatively well and the systems are diverse.