The lack of publicly available high-quality and accurately labeled datasets has long been a
major bottleneck for singing voice synthesis (SVS). To tackle this problem, we present …

Numerous studies in the field of music generation have demonstrated impressive
performance, yet virtually no models are able to directly generate music to match …

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 …

Recent years have witnessed a rapid growth of large-scale language models in the domain
of music audio. Such models enable end-to-end generation of higher-quality music, and …

This survey explores the symbiotic relationship between Machine Learning (ML) and music,
focusing on the transformative role of Artificial Intelligence (AI) in the musical sphere …

Automatic music transcription (AMT) has achieved high accuracy for piano due to the
availability of large, high-quality datasets such as MAESTRO and MAPS, but comparable …

In this paper, we propose a method that uses a combination of the Connectionist Temporal
Classification (CTC) loss and the cross-entropy loss to train a note-level singing transcription …

In this paper, we propose a new dataset named EGDB, that contains transcriptions of the
electric guitar performance of 240 tablatures rendered with different tones. Moreover, we …

Note-level automatic music transcription is one of the most representative music information
retrieval (MIR) tasks and has been studied for various instruments to understand music …

In the age of music streaming platforms, the task of automatically tagging music audio has
garnered significant attention, driving researchers to devise methods aimed at enhancing …