Abstract
We present and release Omnizart, a new Python library that provides a streamlined solution to automatic music transcription (AMT). Omnizart encompasses modules that construct the life-cycle of deep learning-based AMT, and is designed for ease of use with a compact command-line interface. To the best of our knowledge, Omnizart is the first transcription toolkit which offers models covering a wide class of instruments ranging from solo, instrument ensembles, percussion instruments to vocal, as well as models for chord recognition and beat/downbeat tracking, two music information retrieval (MIR) tasks highly related to AMT.
Highlights
We present and release Omnizart, a new Python library that provides a streamlined solution to automatic music transcription (AMT)
Omnizart supports models for chord recognition and beat/downbeat tracking, which are highly related to AMT
The model features a U-net that takes as inputs the audio spectrogram, generalized cepstrum (GC) (Su & Yang, 2015), and GC of spectrogram (GCoS) (Wu et al, 2018), and outputs a multi-channel time-pitch representation with time- and pitch-resolution of 20 ms and 25 cents, respectively
Summary
We present and release Omnizart, a new Python library that provides a streamlined solution to automatic music transcription (AMT). Omnizart encompasses modules that construct the life-cycle of deep learning-based AMT, and is designed for ease of use with a compact command-line interface. To the best of our knowledge, Omnizart is the first toolkit that offers transcription models for various music content including piano solo, instrument ensembles, percussion and vocal. Omnizart supports models for chord recognition and beat/downbeat tracking, which are highly related to AMT. Pre-trained models of chord recognition and beat/downbeat tracking; The main functionalities in the life-cycle of AMT research, covering dataset downloading, feature pre-processing, model training, to the sonification of the transcription result. Omnizart is based on Tensorflow (Abadi et al, 2016). The complete code base, commandline interface, documentation, as well as demo examples can all be accessed from the project website
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