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A. Anantapadmanabhan, J. Bello, R. Krishnan, and H. Murthy, "Tonic-Independent Stroke Transcription of the Mridangam," Paper P2-2, (2014 January.). doi: A. Anantapadmanabhan, J. Bello, R. Krishnan, and H. Murthy, "Tonic-Independent Stroke Transcription of the Mridangam," Paper P2-2, (2014 January.). doi:
Abstract: In this paper, we use a data-driven approach for the tonic-independent transcription of strokes of the mridangam, a South Indian hand drum. We obtain feature vectors that encode tonic-invariance by computing the magnitude spectrum of the constant-Q transform of the audio signal. Then we use Non-negative Matrix Factorization (NMF) to obtain a low-dimensional feature space where mridangam strokes are separable. We make the resulting feature sequence event-synchronous using short-term statistics of feature vectors between onsets, before classifying into a predefined set of stroke labels using Support Vector Machines (SVM). The proposed approach is both more accurate and flexible compared to that of tonic-specific approaches.

@article{anantapadmanabhan2014tonic-independent,
author={anantapadmanabhan, akshay and bello, juan and krishnan, raghav and murthy, hema},
journal={journal of the audio engineering society},
title={tonic-independent stroke transcription of the mridangam},
year={2014},
volume={},
number={},
pages={},
doi={},
month={january},} @article{anantapadmanabhan2014tonic-independent,
author={anantapadmanabhan, akshay and bello, juan and krishnan, raghav and murthy, hema},
journal={journal of the audio engineering society},
title={tonic-independent stroke transcription of the mridangam},
year={2014},
volume={},
number={},
pages={},
doi={},
month={january},
abstract={in this paper, we use a data-driven approach for the tonic-independent transcription of strokes of the mridangam, a south indian hand drum. we obtain feature vectors that encode tonic-invariance by computing the magnitude spectrum of the constant-q transform of the audio signal. then we use non-negative matrix factorization (nmf) to obtain a low-dimensional feature space where mridangam strokes are separable. we make the resulting feature sequence event-synchronous using short-term statistics of feature vectors between onsets, before classifying into a predefined set of stroke labels using support vector machines (svm). the proposed approach is both more accurate and flexible compared to that of tonic-specific approaches.},}

TY - paper
TI - Tonic-Independent Stroke Transcription of the Mridangam
SP - EP -
AU - Anantapadmanabhan, Akshay
AU - Bello, Juan
AU - Krishnan, Raghav
AU - Murthy, Hema
PY - 2014
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - January 2014 TY - paper
TI - Tonic-Independent Stroke Transcription of the Mridangam
SP - EP -
AU - Anantapadmanabhan, Akshay
AU - Bello, Juan
AU - Krishnan, Raghav
AU - Murthy, Hema
PY - 2014
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - January 2014
AB - In this paper, we use a data-driven approach for the tonic-independent transcription of strokes of the mridangam, a South Indian hand drum. We obtain feature vectors that encode tonic-invariance by computing the magnitude spectrum of the constant-Q transform of the audio signal. Then we use Non-negative Matrix Factorization (NMF) to obtain a low-dimensional feature space where mridangam strokes are separable. We make the resulting feature sequence event-synchronous using short-term statistics of feature vectors between onsets, before classifying into a predefined set of stroke labels using Support Vector Machines (SVM). The proposed approach is both more accurate and flexible compared to that of tonic-specific approaches.