Automatic Singing Transcription Based on Encoder-decoder Recurrent Neural Networks with a Weakly-supervised Attention Mechanism

This paper describes neural singing transcription that estimates a sequence of musical notes directly from the audio signal of singing voice in an end-to-end manner without time-aligned training data. A conventional approach to singing transcription is to perform vocal F0 estimation followed by musical note estimation. The performance of this approach, however, is severely limited because the F0 estimation errors propagate to the note estimation step and rich acoustic information cannot be used. In addition, it is difficult and time-consuming to split continuous signals of singing voices into segments corresponding to musical notes for making precise time-aligned transcriptions. To solve these problems, we use an encoder-decoder model with an attention mechanism that can automatically learn an input-output alignment and mapping, even from non-aligned training data. The main challenge of our study is to estimate temporal categories (note values) in addition to instantaneous categories (pitches). We thus propose a novel loss function for the attention weights of time-aligned notes for semi-supervised alignment training. By gradually reducing the weight of the loss function, a better input-output alignment can be learned much more quickly. We showed that our method performed well for isolated singing voice in popular music.