Raga Ornamentation Detection (ROD)
The Raga Ornamentation Detection (ROD) dataset is composed of 212 audio files of varying lengths recorded by two expert musicians. Singer 1 performed 108 recordings, while singer 2 produced 104. In total, the data set contains 4.08 hours of audio. In addition to the audio files, the dataset also
The Raga Ornamentation Detection (ROD) dataset is composed of 212 audio files of varying lengths recorded by two expert musicians. Singer 1 performed 108 recordings, while singer 2 produced 104. In total, the data set contains 4.08 hours of audio. In addition to the audio files, the dataset also includes accompaniments like drone Tanpura and percussion (Tabla). The audio files are labelled in the strong labelling fashion.
For more details, check Readme.md
Email: krsumit@iitk.ac.in for further queries
ROD Dataset: Ornamentation Detection in Indian Classical Vocal Music
ROD Dataset: Ornamentation Detection in Indian Classical Vocal Music
Authors: Sumit Kumar, et al.
Affiliation: Indian Institute of Technology Kanpur
Corresponding Paper: Recognizing Ornamentations in Singing Voices in Indian Art Music
Link to paper: https://ieeexplore.ieee.org/document/11275638
License: Creative Commons Attribution 4.0 International (CC BY 4.0)
Overview:
The Raga Ornamentation Detection (ROD) dataset accompanies the paper titled “Recognizing Ornamentations in Singing Voices in Indian Art Music”.
It comprises professionally recorded and annotated vocal performances collected from two distinct sources:
Expert Teachers: Studio-quality recordings of trained Indian classical vocalists.
Prasar Bharati Broadcast Recordings: Real-world concert and archival recordings.
Each recording is annotated for prominent ornamentations used in Hindustani vocal music, such as meend, kan, andolan, murki, gamak, and nyas swar.
For reproducibility and benchmarking of machine learning models, the dataset includes raw audio recordings as well as precomputed spectrogram features.
Data Description:
Audio Recordings
Spectrograms
Annotations
Two levels of annotations are provided in the dataset.
Ev
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Files are hosted on the source repository. Click download to access the full dataset.