Omkar2118/datasets huggingface dataset:
CAL500exp
Full name: CAL500 Expansion

The **CAL500 Expansion** (**CAL500exp**) dataset is an enriched version of the CAL500 music information retrieval dataset. CAL500exp is designed to facilitate music auto-tagging in a smaller temporal scale. The dataset consists of the same songs split into 3,223 acoustically homogenous segments of 3 to 16 seconds. The tag labels are annotated in the segment level instead of track level. The annotations were obtained from annotators with strong music background.

Source: [Towards time-varying music auto-tagging based on CAL500 expansion](https://doi.org/10.1109/ICME.2014.6890290)
Image Source: [Towards time-varying music auto-tagging based on CAL500 expansion](https://doi.org/10.1109/ICME.2014.6890290)
Audio Source: [http://calab1.ucsd.edu/~datasets/cal500/cal500data/](http://calab1.ucsd.edu/~datasets/cal500/cal500data/)

Introducing paper: Towards time-varying music auto-tagging based on CAL500 expansion
CAL10K
Full name: Computer Audition Lab 10000

The **CAL10K** dataset (introduced as Swat10k) contains 10,870 songs that are weakly-labelled using a tag vocabulary of 475 acoustic tags and 153 genre tags. The tags have all been harvested from [Pandora’s](https://www.pandora.com/) website and result from song annotations performed by expert musicologists involved with the Music Genome Project.

Source: [Exploring automatic music annotation with “acoustically-objectiv” tags](http://modelai.gettysburg.edu/2012/music/docs/Tingle_Autotag_MIR10.pdf)

Introducing paper: Exploring automatic music annotation with “acoustically-objectiv” tags
MuseScore

The **MuseScore** dataset is a collection of 344,166 audio and MIDI pairs downloaded from [MuseScore](https://musescore.org/) website. The audio is usually synthesized by the MuseScore synthesizer. The audio clips have diverse musical genres and are about two mins long on average.

Due to copyright issues the dataset is not publicly available, but can be collected and processed with the provided source code.

Source: [Multitask learning for frame-level instrument recognition](https://arxiv.org/pdf/1811.01143.pdf)
Image Source: [https://biboamy.github.io/instrument-demo/demo.html](https://biboamy.github.io/instrument-demo/demo.html)
Audio Source: [Somewhere over the rainbow](https://biboamy.github.io/instrument-demo/demo.html)

Introducing paper: Multitask learning for frame-level instrument recognition
MTG-Jamendo

The **MTG-Jamendo** dataset is an open dataset for music auto-tagging. The dataset contains over 55,000 full audio tracks with 195 tags categories (87 genre tags, 40 instrument tags, and 56 mood/theme tags). It is built using music available at Jamendo under Creative Commons licenses and tags provided by content uploaders. All audio is distributed in 320kbps MP3 format.

A subset of the dataset is used in the Emotion and Theme Recognition in Music Task within MediaEval 2019.

Source: [https://mtg.github.io/mtg-jamendo-dataset/](https://mtg.github.io/mtg-jamendo-dataset/)
Audio Source: [https://essentia.upf.edu/datasets/mtg-jamendo/raw_30s/audio/](https://essentia.upf.edu/datasets/mtg-jamendo/raw_30s/audio/)

Introducing paper: The MTG-Jamendo Dataset for Automatic Music Tagging
LibriCount

**LibriCount** is a synthetic dataset for speaker count estimation. The dataset contains a simulated cocktail party environment of 0 to 10 speakers, mixed with 0dB SNR from random utterances of different speakers from the LibriSpeech `CleanTest` dataset. All recordings are of 5s durations, and all speakers are active for the most part of the recording.

Source: [https://faroit.com/#libricount](https://faroit.com/#libricount)
Image Source: [https://faroit.com/#libricount](https://faroit.com/#libricount)
Audio Source: [https://zenodo.org/record/1216072](https://zenodo.org/record/1216072)

Introducing paper: LibriCount, a dataset for speaker count estimation (Version v1.0.0)