--- dataset_info: features: - name: filename dtype: string - name: 'true' sequence: float32 length: 20 - name: mask sequence: int32 length: 20 - name: mp3_bytes dtype: binary splits: - name: train num_bytes: 1790991884 num_examples: 14915 - name: test num_bytes: 611455142 num_examples: 5085 download_size: 0 dataset_size: 2402447026 configs: - config_name: default data_files: - split: train path: data/shard_train_* - split: test path: data/shard_test_* --- # CPJKU/openmic The dataset is made available by Spotify AB under a Creative Commons Attribution 4.0 International (CC BY 4.0) license. The full terms of this license are included alongside this dataset. This dataset is preprocessed and compressed to 32khz mp3 files. The bytes of the mp3 files are embedded. The mp3 bytes can be decoded quickly using for [example](https://github.com/kkoutini/PaSST/blob/4519e4605989b8c2e62dccb5b928af9bf7bf8602/audioset/dataset.py#L55) or [minimp3](https://github.com/f0k/minimp3py). Take a look at the original dataset for more information. The original dataset contains the following: 10 second snippets of audio, in a directory format like 'audio/{0:3}/{0}.ogg'.format(sample_key) VGGish features as JSON objects, in a directory format like 'vggish/{0:3}/{0}.json'.format(sample_key) MD5 checksums for each OGG and JSON file Anonymized individual responses, in 'openmic-2018-individual-responses.csv' Aggregated labels, in 'openmic-2018-aggregated-labels.csv' Track metadata, with licenses for each audio recording, in 'openmic-2018-metadata.csv' A Python-friendly NPZ file of features and labels, 'openmic-2018.npz' Sample partitions for train and test, in 'partitions/*.txt' ## Homepage https://zenodo.org/records/1432913 ## Citation ``` Humphrey, Eric J., Durand, Simon, and McFee, Brian. "OpenMIC-2018: An Open Dataset for Multiple Instrument Recognition." in Proceedings of the 19th International Society for Music Information Retrieval Conference (ISMIR), 2018. ``` ## License CC BY 4.0