Indian Art Music Tonic Datasets

Introduction

These datasets comprise audio excerpts and manually done annotations of the tonic pitch of the lead artist for each audio excerpt. Each excerpt is accompanied by its associated editorial metadata. These datasets can be used to develop and evaluate computational approaches for automatic tonic identification in Indian art music. These datasets have been used in several articles mentioned below. A majority of these datasets come from the CompMusic corpora of Indian art music, for which each recording is associated with a MBID. With the MBID other information can be obtained using the Dunya API. We here provide an overview of the tonic identification datasets. 

Datasets 

The statistics about the datasets for tonic identification is listed in the table below. These six datasets are used in [1] for a comparative evaluation. To the best of our knowledge these are the largest datasets available for tonic identification for Indian art music. These datases vary in terms of the audio quality, recording period (decade), the number of recordings for Carnatic, Hindustani, male and female singers and instrumental and vocal excerpts. For a detailed information about these datasets we refer to Chapter 3 of this thesis.

All the datasets (annotations) are version controlled. To know how the features are extracted visit the companion page for the publication.

The audio files corresponding to these datsets are made available on request for only research purposes. To obtain the files, please refer to this Zenodo entry.

Annotation Format 

The tonic annotations are availabe both in tsv and json format. 

TSV:  

JSON: {
       'artist': , 

       'filepath': ,

        'gender': ,

        'mbid': ,

        'tonic': ,

        'tradition': ,

        'type':
     }

where keys of the main dictionary are the filepaths to the audio files (feature path is exactly the same with a different extension of the file name).

Using this dataset

If you use this dataset in a publication, please cite:

Gulati, S., Bellur, A., Salamon, J., Ranjani, H. G., Ishwar, V., Murthy, H. A., & Serra, X. (2014). Automatic Tonic Identification in Indian Art Music: Approaches and Evaluation. Journal of New Music Research, 43(01), 55–73. 

http://hdl.handle.net/10230/25675

We are interested in knowing if you find our datasets useful! If you use our dataset please email us at [e-mail] and tell us about your research.

Contact 

If you have any questions or comments about the dataset, please feel free to email: [sankalp (dot) gulati (at) gmail (dot) com], or [sankalp (dot) gulati (at) upf (dot) edu]

http://compmusic.upf.edu/iam-tonic-dataset