  We present a framework based on neural networks to extract music scores
directly from polyphonic audio in an end-to-end fashion. Most previous
Automatic Music Transcription (AMT) methods seek a piano-roll representation of
the pitches, that can be further transformed into a score by incorporating
tempo estimation, beat tracking, key estimation or rhythm quantization. Unlike
these methods, our approach generates music notation directly from the input
audio in a single stage. For this, we use a Convolutional Recurrent Neural
Network (CRNN) with Connectionist Temporal Classification (CTC) loss function
which does not require annotated alignments of audio frames with the score
rhythmic information. We trained our model using as input Haydn, Mozart, and
Beethoven string quartets and Bach chorales synthesized with different tempos
and expressive performances. The output is a textual representation of
four-voice music scores based on **kern format. Although the proposed approach
is evaluated in a simplified scenario, results show that this model can learn
to transcribe scores directly from audio signals, opening a promising avenue
towards complete AMT.
