Music Transcription

Automatic music transcription (AMT) aims to convert audio recordings of music into symbolic representations like musical notation, a crucial task in music information retrieval. Current research heavily utilizes deep learning, focusing on advancements in transformer-based architectures, convolutional recurrent neural networks, and other neural network designs to improve accuracy and efficiency, particularly for polyphonic music and multiple instruments. These improvements are driven by the development of larger, higher-quality datasets and novel training techniques like data augmentation and transfer learning across instruments. The resulting advancements have significant implications for music analysis, education, and the creation of new music technologies.