Music transcription is the process of transforming recorded sound of musical performances
into symbolic representations such as sheet music or MIDI files. Extensive research and …

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 …

We propose Beat Transformer, a novel Transformer encoder architecture for joint beat and
downbeat tracking. Different from previous models that track beats solely based on the …

In this paper, we address the beat tracking task which is to predict beat times corresponding
to the input audio. Due to the long sequential inputs, it is still challenging to model the global …

Optical Music Recognition (OMR) is an important technology in music and has been
researched for a long time. Previous approaches for OMR are usually based on CNN for …

Automatic Music Transcription (AMT) aims to convert music audio into digital sheet music.
Piano transcription is a popular but challenging subtask of AMT. For every piano pitch, the …

Weakly supervised video anomaly detection (WS-VAD) is often formulated as a multiple
instance learning (MIL) problem. Snippet-level anomaly scores can be predicted using only …

The intelligent assessment of musical instrument fingerings can provide learners with timely
feedback to greatly improve learning efficiency and lay the foundation for distance teaching …

Deep supervised learning is the most common way of automatically music generation.
However, this sort of model only learns probabilities from dataset, and such pattern does not …

Piano transcription is a significant problem in the field of music information retrieval, aiming
to obtain symbolic representations of music from captured audio or visual signals. Previous …