by Siddharth Gururani
Instrument recognition as a task in Music Information Retrieval has had a long history and several datasets have been introduced for public use. The RWC dataset and the UIOWA dataset, for instance, are standard datasets for evaluation of instrument recognition in monophonic audio. The IRMAS dataset is a large dataset for predominant instrument detection. There are however, not many datasets available for instrument detection in polyphonic mixtures.
Muti-track data comes in handy for such a task. Multi-track datasets contain the recording sessions of songs, which will normally include the raw tracks, the stems, and the final mix. This enables the usage of multi-track datasets for a variety of tasks such as source separation and multi-f0 tracking, but also instrument recognition.
MedleyDB is a widely known dataset that contains 250 multi-tracks with a well defined annotation format and instrument taxonomy. While this might be considered an overwhelming amount of data, new data-hungry algorithms such as deep neural networks are often in need of more data for training and testing. We release a new set of annotated multi-track data in a format that is compatible to MedleyDB. It contains 258 multi-tracks originating from the website for a book titled “Mixing Secrets For the Small Studio.”