overview of research at iitb
play

Overview of Research at IITB Computational studies on Hindustani - PowerPoint PPT Presentation

Overview of Research at IITB Computational studies on Hindustani music CompMusic Workshop, Chennai 2013 Preeti Rao Department of Electrical Engineering I.I.T. Bombay 1 Some goals Automatic tagging of audio by genre, style, raga,


  1. Overview of Research at IITB Computational studies on Hindustani music CompMusic Workshop, Chennai 2013 Preeti Rao Department of Electrical Engineering I.I.T. Bombay 1

  2. Some goals • Automatic “tagging” of audio by genre, style, raga, tala and other discovered descriptors and relationships • Automatic creation of “navigation layer” for concert audio • Facilitating search for musically relevant objects such as melodic and rhythmic motifs • Building tools that facilitate musicological research on performance practices Common to all the above: Need for a music representation (aka features) and similarity measure (for classification) 2

  3. Music CD cover information … (YouTube has even less!) 3

  4. Hindustani music descriptors/tags • Artiste (instrument), accompanists • Genre (dhrupad, khyal, tarana … ), sub-genre (gharana) • Concert structure and sections with timing – Bada khyal, Chhota khyal • Bandish: alap, vistaar, taan • Raga, Tala, Laya of major sections • Composition (bandish, identified by mukhda) The question Can the descriptions be obtained by audio content analysis and possibly enhanced with contextual semantic information ? 4

  5. Kishori Amonkar Deshkar: bada khyal (vistaar, taan) Alap (slow tempo) Alap (medium tempo) Taan 5

  6. Kishori Amonkar Deshkar: bada khyal taan, chhota khyal taan Taan (madhylaya) Taan (drut laya) 6

  7. Kishori Amonkar Deshkar vistaar taan chhota khyal alap (bada khyal) (bada khyal) 7

  8. Uday Bhawalkar (dhrupad) Yaman Alap Jod (alap) Jhala (alap) 8

  9. Uday Bhawalkar (dhrupad) Yaman alap jod jhala 9

  10. Kishori Amonkar: Deshkar, Gaud-Sarang Raga Deshkar Raga Gaud Sarang 10

  11. original resynthesized Raga Deshkar Raga Gaud Sarang 11

  12. Raga Deshkar P P P P G G G G G G R S R R S R S R S S R S S D Raga Gaud Sarang G S R S D S S R R S S S D S D D D 12

  13. Raga characteristics from pitch distribution Pt. Vidhyadhar Vyas Marwa and Puriya (share the same swaras) 13

  14. Melodic motif ( mukhda ) detection Kishori Amonkar, Deshkar, Tintal 14 Bandish: Piya Jaag

  15. Phrase duration, dependence on tempo 15

  16. Within-concert variability of motif 16

  17. Within-concert variability across concerts Intra-phrase class distance distribution Kafi 17

  18. Ontology for Indian music Learning metadata (textual) from forums by using NLP techniques to learn relationships between entities. • Augment audio-based music ontology for Indian music with information extracted from online music forums to achieve superior retrieval systems for Indian music. Example: Get songs with phrase ‘NDNP’ and sung by a disciple of D.K. Pattammal • Challenges: text sources are unstructured, ungrammatical … 18

  19. Summary • High-level musical attributes (melody, rhythm) can be derived from low-level acoustic parameters such as pitch and onsets extracted by audio signal processing. • The audio signal processing is challenging due to the mixture of several instruments, strong diversity in the characteristics and the highly time-varying nature. • Melody and rhythm representations that are musicologically informed can be useful in the description of music recordings. • Knowledge can be very helpful. Creating these from available sources is a challenge. 19

  20. Coming up … • Vedhas Pandit, Kaustuv : Characterization of melodic motifs • Vinutha T. P.: Rhythmic structure based segmentation • Joe Cheri Ross: Ontology for Indian Music: An Approach for ontology learning from online music forums • Amruta J. Vidwans and Prateek Verma: Melodic style detection in Hindustani music 20

  21. Thank you 21 Department of Electrical Engineering , IIT Bombay

Recommend


More recommend