In this paper we propose a new approach for tonic identification in Indian art music and present a proposal for a/ncomplete iterative system for the same. Our method splits the task of tonic pitch identification into two stages. In the first stage, which is applicable to both vocal and instrumental music, we perform a multi-pitch analysis of the audio signal to identify the tonic pitch-class. Multi-pitch analysis/nallows us to take advantage of the drone sound, which constantly/nreinforces the tonic. ...
In this paper we propose a new approach for tonic identification in Indian art music and present a proposal for a/ncomplete iterative system for the same. Our method splits the task of tonic pitch identification into two stages. In the first stage, which is applicable to both vocal and instrumental music, we perform a multi-pitch analysis of the audio signal to identify the tonic pitch-class. Multi-pitch analysis/nallows us to take advantage of the drone sound, which constantly/nreinforces the tonic. In the second stage we estimate the octave in which the tonic of the singer lies and is thus/nneeded only for the vocal performances. We analyse the predominant melody sung by the lead performer in order to establish the tonic octave. Both stages are individually evaluated on a sizable music collection and are shown to/nobtain a good accuracy. We also discuss the types of errors made by the method./nFurther, we present a proposal for a system that aims to incrementally utilize all the available data, both audio and metadata in order to identify the tonic pitch. It produces a tonic estimate and a confidence value, and is iterative in nature. At each iteration, more data is fed into the system/nuntil the confidence value for the identified tonic is above a defined threshold. Rather than obtain high overall accuracy for our complete database, ultimately our goal is to develop a system which obtains very high accuracy on a subset of the database with maximum confidence.
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