Transcription Model

Automatic music transcription (AMT) aims to convert audio recordings into symbolic musical representations, a challenging task due to the complexity of polyphonic music and the diversity of instruments. Current research heavily utilizes transformer-based architectures, often enhanced with techniques like hierarchical attention and mixture-of-experts models, to improve accuracy and address data limitations through methods such as data augmentation and cross-dataset training. These advancements are driving improvements in transcription quality across various instruments and genres, impacting music information retrieval, music education, and potentially assistive technologies for musicians. Furthermore, efforts are underway to develop more musically-informed evaluation metrics beyond simple accuracy measures, leading to a more nuanced understanding of transcription model performance.