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, 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

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

3 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 Kishori Amonkar Deshkar: bada khyal (vistaar, taan)

Alap (slow tempo)

Alap (medium tempo)

Taan

5 Kishori Amonkar Deshkar: bada khyal taan, chhota khyal taan

Taan (madhylaya)

Taan (drut laya)

6 Kishori Amonkar Deshkar

vistaar alap taan chhota khyal (bada khyal) (bada khyal) 7 Uday Bhawalkar (dhrupad) Yaman

Alap

Jod (alap)

Jhala (alap)

8 Uday Bhawalkar (dhrupad) Yaman

alap jod jhala

9 Kishori Amonkar: Deshkar, Gaud-Sarang Raga Deshkar

Raga Gaud Sarang

10 original resynthesized Raga Deshkar

Raga Gaud Sarang

11 Raga Deshkar

P P P P G G G G G G R S R R S R S S R S S D R S

Raga Gaud Sarang

R G S S S S S S S R D D S R D D D 12 Raga characteristics from pitch distribution

Pt. Vidhyadhar Vyas Marwa and Puriya (share the same swaras)

13 Melodic motif (mukhda) detection

Kishori Amonkar, Deshkar, Tintal Bandish: Piya Jaag 14 Phrase duration, dependence on tempo

15 Within-concert variability of motif

16 Within-concert variability across concerts Intra-phrase class distance distribution

Kafi

17 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 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 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 Thank you

Department of Electrical Engineering , IIT Bombay 21