Music Transcription by Deep Learning with Data and "Artificial Semantic" Augmentation
This addresses music transcription for audio processing, but it appears incremental as it builds on previous single note recognition work.
The paper tackles music transcription by deep learning, proposing data and 'artificial semantic' augmentation methods to enhance efficiency for monophonic and polyphonic note recognition, with results showing improvements through increased training data dimensions and transformations.
In this progress paper the previous results of the single note recognition by deep learning are presented. The several ways for data augmentation and "artificial semantic" augmentation are proposed to enhance efficiency of deep learning approaches for monophonic and polyphonic note recognition by increase of dimensions of training data, their lossless and lossy transformations.