Automatic drum transcription is a critical tool in Music Information Retrieval for extracting and
analyzing the rhythm of a music track, but it is limited by the size of the datasets available for …

Within the broad problem known as automatic music transcription, we considered the
specific task of automatic drum transcription (ADT). This is a complex task that has recently …

In the era of data-driven Music Information Retrieval (MIR), the scarcity of labeled data has
been one of the major concerns to the success of an MIR task. In this work, we leverage the …

Algorithms for automatic piano transcription have improved dramatically in recent years due
to new datasets and modeling techniques. Recent developments have focused primarily on …

With this thesis, we aim to automate the creation of DJ mixes. A DJ mix consists of an
uninterrupted sequence of music, constructed by playing tracks one after the other, to …

Multi-pitch estimation is a decades-long research problem involving the detection of pitch
activity associated with concurrent musical events within multi-instrument mixtures …

We demonstrate that pretrained generative models can learn representations that are useful
for multi-pitch estimation. We explore representations extracted from DAC, a state-of-the-art …

Automatic Music Transcription (AMT) is a vital technology in the field of music information
processing. Despite recent enhancements in performance due to machine learning …