The utilization of deep learning techniques in generating various contents (such as image,
text, etc.) has become a trend. Especially music, the topic of this paper, has attracted …

In recent years, foundation models (FMs) such as large language models (LLMs) and latent
diffusion models (LDMs) have profoundly impacted diverse sectors, including music. This …

Symbolic music understanding, which refers to the understanding of music from the symbolic
data (eg, MIDI format, but not audio), covers many music applications such as genre …

Automatic Music Transcription (AMT), inferring musical notes from raw audio, is a
challenging task at the core of music understanding. Unlike Automatic Speech Recognition …

While there are many music datasets with emotion labels in the literature, they cannot be
used for research on symbolic-domain music analysis or generation, as there are usually …

Automatic Music Transcription has seen significant progress in recent years by training
custom deep neural networks on large datasets. However, these models have required …

Symbolic music datasets are important for music information retrieval and musical analysis.
However, there is a lack of large-scale symbolic datasets for classical piano music. In this …

This paper presents an attempt to employ the mask language modeling approach of BERT
to pre-train a 12-layer Transformer model over 4,166 pieces of polyphonic piano MIDI files …

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 introduce CLaMP: Contrastive Language-Music Pre-training, which learns cross-modal
representations between natural language and symbolic music using a music encoder and …