Automatic Transcription of Drum Strokes in Carnatic Music

The mridangam is a double-headed percussion instrument that plays a key role in Carnatic music concerts. This paper presents a novel automatic transcription algorithm to classify the strokes played on the mridangam. Onset detection is first performed to segment the audio signal into individual strokes, and feature vectors consisting of the DFT magnitude spectrum of the segmented signal are generated. A multi-layer feedforward neural network is trained using the feature vectors as inputs and the manual transcriptions as targets. Since the mridangam is a tonal instrument tuned to a given tonic, tonic invariance is an important feature of the classifier. Tonic invariance is achieved by augmenting the dataset with pitch-shifted copies of the audio. This algorithm consistently yields over 83% accuracy on a held-out test dataset.Comment: 7 pages, 9 figure

Akshara transcription of mrudangam strokes in carnatic music

Percussion instruments play a significant role in/nCarnatic music concerts. The percussion artist enjoys a great/ndegree of freedom in improvising within the defined tala/nstructure/nof a composition. The objective of this paper is to transcribe the/nimprovisations, treating the percussion strokes as syllables or/naksharas./nOnset detection is performed to segment the waveform at/neach/nakshara/n. Using the transcriptions from the training data,/na three-state Hidden Markov Model is built for each/nakshara/n./nThe language model is derived from the training data. Testing/nis also performed isolated style using onset detection to segment/nthe phrase, and the language model to correct the transcription./nTranscription is performed on both concert recordings and studio/nrecordings. This technique yields upto/n≈/n96%/naccuracy on studio/nrecordings and/n≈/n76%/naccuracy for concert recordings./nAs the mrudangam/n1/nis an instrument that is based on tonic;/ntonic normalised features, namely, Cent Filterbank Cepstral/ncoefficients are used. It is shown that tonic normalisation helps/nin transcription across different tonics.This research was partly funded by the European Research/nCouncil under the European Unions Seventh Framework Pro-/ngram, as part of the CompMusic project (ERC grant agreement/n267583)