  Automatic music transcription (AMT) aims to convert raw audio to symbolic
music representation. As a fundamental problem of music information retrieval
(MIR), AMT is considered a difficult task even for trained human experts due to
overlap of multiple harmonics in the acoustic signal. On the other hand, speech
recognition, as one of the most popular tasks in natural language processing,
aims to translate human spoken language to texts. Based on the similar nature
of AMT and speech recognition (as they both deal with tasks of translating
audio signal to symbolic encoding), this paper investigated whether a generic
neural network architecture could possibly work on both tasks. In this paper,
we introduced our new neural network architecture built on top of the current
state-of-the-art Onsets and Frames, and compared the performances of its
multiple variations on AMT task. We also tested our architecture with the task
of speech recognition. For AMT, our models were able to produce better results
compared to the model trained using the state-of-art architecture; however,
although similar architecture was able to be trained on the speech recognition
task, it did not generate very ideal result compared to other task-specific
models.
