Automatic piano transcription with frequency attention and time attention fusion mechanism

With the development of deep learning, automatic music transcription has witnessed significant advancements in recent years. In this work, we develop an attention-based model with a fusion mechanism to capture and integrate information from time and frequency domains. Furthermore, we adopt a separation strategy to avoid mutual interference among different branches in a multi-task model. Experiments conducted on the MAESTRO dataset demonstrate that our proposed model achieves state-of-the-art transcription performance across multiple metrics among existing multi-task models.