• DocumentCode
    2427734
  • Title

    Akshara transcription of mrudangam strokes in Carnatic music

  • Author

    Kuriakose, Jom ; Chaitanya Kumar, J. ; Sarala, Padi ; Murthy, Hema A. ; Sivaraman, Umayalpuram K.

  • Author_Institution
    Indian Inst. of Technol., Madras, Chennai, India
  • fYear
    2015
  • fDate
    Feb. 27 2015-March 1 2015
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    Percussion instruments play a significant role in Carnatic music concerts. The percussion artist enjoys a great degree of freedom in improvising within the defined tala structure of a composition. The objective of this paper is to transcribe the improvisations, treating the percussion strokes as syllables or aksharas. Onset detection is performed to segment the waveform at each akshara. Using the transcriptions from the training data, a three-state Hidden Markov Model is built for each akshara. The language model is derived from the training data. Testing is also performed isolated style using onset detection to segment the phrase, and the language model to correct the transcription. Transcription is performed on both concert recordings and studio recordings. This technique yields upto ≈ 96% accuracy on studio recordings and ≈ 76% accuracy for concert recordings. As the mrudangam1 is an instrument that is based on tonic; tonic normalised features, namely, Cent Filterbank Cepstral coefficients are used. It is shown that tonic normalisation helps in transcription across different tonics.
  • Keywords
    cepstral analysis; channel bank filters; hidden Markov models; musical instruments; natural language processing; Akshara transcription; Carnatic music concerts; Mrudangam strokes; aksharas; cent filterbank cepstral coefficients; composition tala structure; concert recordings; isolated style; language model; onset detection; percussion instruments; percussion strokes; phrase segmentation; studio recordings; syllable; three-state hidden Markov model; tonic normalisation; training data; waveform segmentation; Accuracy; Computational modeling; Databases; Feature extraction; Hidden Markov models; Instruments; Mel frequency cepstral coefficient;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Communications (NCC), 2015 Twenty First National Conference on
  • Conference_Location
    Mumbai
  • Type

    conf

  • DOI
    10.1109/NCC.2015.7084906
  • Filename
    7084906