Indian Art Music Raga Recognition Dataset (features)

<p>The <strong>Rāga Recognition Datasets (features)</strong> comprise two sizable datasets, one for each music tradition: the <strong>Carnatic Music Dataset (CMD)</strong> and the <strong>Hindustani Music Dataset (HMD)</strong>. Each dataset entry includes features such as <strong>pitch</strong>, <strong>tonic</strong>, and <strong>nyas</strong> and <strong>tani</strong> segments. These datasets can be used to develop and evaluate approaches for automatic rāga recognition in Indian art music. To the best of our knowledge, they are the largest and most comprehensive datasets (in terms of available metadata) ever used for studying this task.</p> <p>This repository only contains the metadata and computed features for the dataset, and shared in open access. To get the audio, please refer&nbsp;<a href="https://zenodo.org/records/7278511" target="_blank" rel="noopener">to this zenodo entry</a> and submit your request.</p> <p>&nbsp;</p> <p>Please cite the following publications if you use the material shared here in your research work.</p> <blockquote> <p>Gulati, S., Serr&agrave;, J., Ganguli, K. K., &cedil;Sent&uuml;rk, S., &amp; Serra, X. (2016). Time-delayed melody surfaces for raga recognition. In Proceedings of the 17th International Society for Music Information Retrieval Conference (ISMIR), pp. 751&ndash;757. New York, USA. [<a href="http://hdl.handle.net/10230/33117">Postprint PDF</a>]</p> </blockquote> <blockquote> <p>Gulati, S., Serr&agrave;, J., Ishwar, V., &cedil;Sent&uuml;rk, S., &amp; Serra, X. (2016). Phrase-based raga recognition using vector space modeling. In Proceedings of the 41st IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 66&ndash;70. Shanghai, China. [<a href="http://hdl.handle.net/10230/32879">Postprint PDF</a>]</p> </blockquote> <p>&nbsp;</p> <h2>Annotation Format</h2> <p>We provide both tsv files and json files that contain information about each audio recording in terms of its mbid, the path of the audio/feature files and the associated rāga identifier. Each rāga is assigned a unique identifier by Dunya, which is similar to the mbid in terms of purpose. We also provide a mapping of the rāga id to its transliterated name.</p> <h2>Mirdata</h2> <p>This dataset is included in <a href="https://github.com/mir-dataset-loaders/mirdata">mirdata</a>. Use the following code snippet to access the dataset in mirdata.</p> <pre><code># Import midata import mirdata # Initialize dataset dataset_name = 'compmusic_raga' data_home = 'mirdata/dataset' dataset = mirdata.initialize(dataset_name, data_home=data_home) # Download dataset dataset.download() # Validate dataset dataset.validate() # Load dataset as a dictionary with track ids as keys and track objects as values data = dataset.load_tracks()</code></pre> <p>In order to load the audio files in mirdata, they must be requested beforehand and placed in the data home directory.</p> <h2>Contact&nbsp;</h2> <p>If you have any questions or comments about the dataset, please feel free to email:</p> <p><a href="mailto:mtg-info@upf.edu">mtg-info@upf.edu</a></p> <p>&nbsp;</p>