Automatic Piano Transcription with Hierarchical Frequency-Time Transformer
This addresses the problem of accurately transcribing polyphonic piano music for applications in music analysis and generation, representing an incremental improvement over existing methods.
The paper tackled automatic piano transcription by proposing hFT-Transformer, a hierarchical frequency-time Transformer architecture, and it achieved state-of-the-art performance on F1-scores across multiple metrics using MAPS and MAESTRO v3.0.0 datasets.
Taking long-term spectral and temporal dependencies into account is essential for automatic piano transcription. This is especially helpful when determining the precise onset and offset for each note in the polyphonic piano content. In this case, we may rely on the capability of self-attention mechanism in Transformers to capture these long-term dependencies in the frequency and time axes. In this work, we propose hFT-Transformer, which is an automatic music transcription method that uses a two-level hierarchical frequency-time Transformer architecture. The first hierarchy includes a convolutional block in the time axis, a Transformer encoder in the frequency axis, and a Transformer decoder that converts the dimension in the frequency axis. The output is then fed into the second hierarchy which consists of another Transformer encoder in the time axis. We evaluated our method with the widely used MAPS and MAESTRO v3.0.0 datasets, and it demonstrated state-of-the-art performance on all the F1-scores of the metrics among Frame, Note, Note with Offset, and Note with Offset and Velocity estimations.