Medley-solos-DB ============= Version 1.1, March 2019. Created By -------------- Vincent Lostanlen (1), Carmine-Emanuele Cella (2), Rachel Bittner (3), Slim Essid (4). (1): New York University (2): UC Berkeley (3): Spotify, Inc. (4): Télécom ParisTech Description --------------- Medley-solos-DB is a cross-collection dataset for automatic musical instrument recognition in solo recordings. It consists of a training set of 3-second audio clips, which are extracted from the MedleyDB dataset of Bittner et al. (ISMIR 2014) as well as a test set set of 3-second clips, which are extracted from the solosDB dataset of Essid et al. (IEEE TASLP 2009). Each of these clips contains a single instrument among a taxonomy of eight: clarinet, distorted electric guitar, female singer, flute, piano, tenor saxophone, trumpet, and violin. The Medley-solos-DB dataset is the dataset that is used in the benchmarks of musical instrument recognition in the publications of Lostanlen and Cella (ISMIR 2016) and Andén et al. (IEEE TSP 2019). [1] V. Lostanlen, C.E. Cella. Deep convolutional networks on the pitch spiral for musical instrument recognition. Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), 2016. [2] J. Andén, V. Lostanlen S. Mallat. Joint time-frequency scattering. IEEE Transactions in Signal Processing. 2019, to appear. Data Files -------------- The Medley-solos-DB contains 21572 audio clips as WAV files, sampled at 44.1 kHz, with a single channel (mono), at a bit depth of 32. Every audio clip has a fixed duration of 2972 milliseconds, that is, 65536 discrete-time samples. Every audio file has a name of the form: Medley-solos-DB_SUBSET-INSTRUMENTID_UUID.wav For example: Medley-solos-DB_test-0_0a282672-c22c-59ff-faaa-ff9eb73fc8e6.wav corresponds to the snippet whose universally unique identifier (UUID) is 0a282672-c22c-59ff-faaa-ff9eb73fc8e6, contains clarinet sounds (clarinet has instrument id equal to 0), and belongs to the test set. Metadata Files ------------------- The Medley-solos-DB_metadata is a CSV file containing 21572 rows (one for each audio clip) and five columns: 1. subset: either "training", "validation", or "test" 2. instrument: tag in Medley-DB taxonomy, such as "clarinet", "distorted electric guitar", etc. 3. instrument id: integer from 0 to 7. There is a one-to-one between "instrument" (string format) and "instrument id" (integer). We provide both for convenience. 4. track id: integer from 0 to 226. The track and artist names are anonymized. 5. UUID: universally unique identifier. Assigned and random, and different for every row. The list of instrument classes is: 0. clarinet 1. distorted electric guitar 2. female singer 3. flute 4. piano 5. tenor saxophone 6. trumpet 7. violin Please acknowledge Medley-solos-DB in academic research --------------------------------------------------------------------------------- When Medley-solos-DB is used for academic research, we would highly appreciate it if scientific publications of works partly based on this dataset cite the following publication: V. Lostanlen, C.E. Cella. Deep convolutional networks on the pitch spiral for musical instrument recognition. Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), 2016. The creation of this dataset was supported by ERC InvariantClass grant 320959. Conditions of Use ------------------------ Dataset created by Vincent Lostanlen, Rachel Bittner, and Slim Essid, as a derivative work of Medley-DB and solos-Db. The Medley-solos-DB dataset is offered free of charge under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) license: https://creativecommons.org/licenses/by/4.0/ The dataset and its contents are made available on an "as is" basis and without warranties of any kind, including without limitation satisfactory quality and conformity, merchantability, fitness for a particular purpose, accuracy or completeness, or absence of errors. Subject to any liability that may not be excluded or limited by law, the authors are not liable for, and expressly exclude all liability for, loss or damage however and whenever caused to anyone by any use of the Medley-solos-DB dataset or any part of it. Feedback ------------- Please help us improve Medley-solos-DB by sending your feedback to: vincent.lostanlen@nyu.edu In case of a problem, please include as many details as possible. Acknowledgement ------------------------- We thank all artists, recording engineers, curators, and annotators of both MedleyDB and solosDb.