  We present an automatic piano transcription system that converts polyphonic
audio recordings into musical scores. This has been a long-standing problem of
music information processing, and recent studies have made remarkable progress
in the two main component techniques: multipitch detection and rhythm
quantization. Given this situation, we study a method integrating
deep-neural-network-based multipitch detection and statistical-model-based
rhythm quantization. In the first part, we conducted systematic evaluations and
found that while the present method achieved high transcription accuracies at
the note level, some global characteristics of music, such as tempo scale,
metre (time signature), and bar line positions, were often incorrectly
estimated. In the second part, we formulated non-local statistics of pitch and
rhythmic contents that are derived from musical knowledge and studied their
effects in inferring those global characteristics. We found that these
statistics are markedly effective for improving the transcription results and
that their optimal combination includes statistics obtained from separated hand
parts. The integrated method had an overall transcription error rate of 7.1%
and a downbeat F-measure of 85.6% on a dataset of popular piano music, and the
generated transcriptions can be partially used for music performance and
assisting human transcribers, thus demonstrating the potential for practical
applications.
