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The goal of this paper is twofold. First, we introduce DALI, a large and richmultimodal dataset containing 5358 audio tracks with their time-aligned vocalmelody notes and lyrics at four levels of granularity. The second goal is toexplain our methodology where dataset creation and learning models interactusing a teacher-student machine learning paradigm that benefits each other. Westart with a set of manual annotations of draft time-aligned lyrics and notesmade by non-expert users of Karaoke games. This set comes without audio.Therefore, we need to find the corresponding audio and adapt the annotations toit. To that end, we retrieve audio candidates from the Web. Each candidate isthen turned into a singing-voice probability over time using a teacher, a deepconvolutional neural network singing-voice detection system (SVD), trained oncleaned data. Comparing the time-aligned lyrics and the singing-voiceprobability, we detect matches and update the time-alignment lyricsaccordingly. From this, we obtain new audio sets. They are then used to trainnew SVD students used to perform again the above comparison. The process couldbe repeated iteratively. We show that this allows to progressively improve theperformances of our SVD and get better audio-matching and alignment.

DALI: a large Dataset of synchronized Audio, LyrIcs and notes, automatically created using teacher-student machine learning paradigm