Music Information Retrieval
61 papers with code • 0 benchmarks • 17 datasets
These leaderboards are used to track progress in Music Information Retrieval
Since the vocal component plays a crucial role in popular music, singing voice detection has been an active research topic in music information retrieval.
To this end, an established classification architecture, a Convolutional Recurrent Neural Network (CRNN), is applied to the artist20 music artist identification dataset under a comprehensive set of conditions.
In this article, we create a GiantMIDI-Piano (GP) dataset containing 38, 700, 838 transcribed notes and 10, 855 unique solo piano works composed by 2, 786 composers.
This data set is the first publicly available set in OMR research with sufficient size to train and evaluate deep learning models.
Following their success in Computer Vision and other areas, deep learning techniques have recently become widely adopted in Music Information Retrieval (MIR) research.
To date, the best performing techniques, such as the pYIN algorithm, are based on a combination of DSP pipelines and heuristics.
Deep neural networks (DNNs) are successfully applied in a wide variety of music information retrieval (MIR) tasks but their predictions are usually not interpretable.
Most of existing audio fingerprinting systems have limitations to be used for high-specific audio retrieval at scale.