  Since the vocal component plays a crucial role in popular music, singing
voice detection has been an active research topic in music information
retrieval. Although several proposed algorithms have shown high performances,
we argue that there still is a room to improve to build a more robust singing
voice detection system. In order to identify the area of improvement, we first
perform an error analysis on three recent singing voice detection systems.
Based on the analysis, we design novel methods to test the systems on multiple
sets of internally curated and generated data to further examine the pitfalls,
which are not clearly revealed with the current datasets. From the experiment
results, we also propose several directions towards building a more robust
singing voice detector.
