Cultivating vocal activity detection for music audio signals in a circulation-type crowdsourcing ecosystem

This paper presents a crowdsourcing-based self-improvement framework of vocal activity detection (VAD) for music audio signals. A standard approach to VAD is to train a vocal-and-non-vocal classifier by using labeled audio signals (training set) and then use that classifier to label unseen signals. Using this technique, we have developed an online music-listening service called Songle that can help users better understand music by visualizing automatically estimated vocal regions and pitches of arbitrary songs existing on the Web. The accuracy of VAD is limited, however, because in general the acoustic characteristics of the training set are different from those of real songs on the Web. To overcome this limitation, we adapt a classifier by leveraging vocal regions and pitches corrected by volunteer users. UnlikeWikipedia-type crowdsourcing, our Songle-based framework can amplify user contributions: error corrections made for a limited number of songs improve VAD for all songs. This gives better music listening experiences to all users as non-monetary rewards.