In this paper we use a Non-negative Matrix Factorization/n(NMF) based approach to analyze the strokes of the mri-/ndangam, a South Indian hand drum, in terms of the normal/nmodes of the instrument. Using NMF, a dictionary of spectral/nbasis vectors are first created for each of the modes of the/nmridangam. The composition of the strokes are then studied/nby projecting them along the direction of the modes using/nNMF. We then extend this knowledge of each stroke in terms/nof its basic modes to transcribe audio recordings. Hidden/nMarkov Models are adopted to learn the modal activations for/neach of the strokes of the mridangam, yielding up to/n88,40%/naccuracy during transcription.This research...
In this paper, a new approach for automatic audio classification using non-negative matrix factoriza...
International audiencePolyphonic pitch transcription consists of estimating the onset time, duration...
The Mridangam Tani-avarthanam dataset is a transcribed collection of two tani-avarthanams played by ...
In this paper we use a Non-negative Matrix Factorization/n(NMF) based approach to analyz...
In this paper, we use a data-driven approach for the tonic-independent transcription of strokes of t...
The audio examples were recorded from a professional Carnatic percussionist in a semi-anechoic studi...
The Mridangam Stroke dataset is a collection of 7162 audio examples of individual strokes of the Mri...
The mridangam is a double-headed percussion instrument that plays a key role in Carnatic music conce...
Percussion instruments play a significant role in/nCarnatic music concerts. The percussion...
Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Non-negative matrix factorizatio...
Abstract — In this paper, a class of algorithms for automatic classification of individual musical i...
Given a musical audio recording, the goal of music transcription is to determine a score-like repres...
Given a musical audio recording, the goal of music transcription is to determine a score-like repres...
This paper proposes a real-time capable method for transcribing and separating occurrences of single...
Automatic Music Transcription (AMT) is concerned with the problem of producing the pitch content of ...
In this paper, a new approach for automatic audio classification using non-negative matrix factoriza...
International audiencePolyphonic pitch transcription consists of estimating the onset time, duration...
The Mridangam Tani-avarthanam dataset is a transcribed collection of two tani-avarthanams played by ...
In this paper we use a Non-negative Matrix Factorization/n(NMF) based approach to analyz...
In this paper, we use a data-driven approach for the tonic-independent transcription of strokes of t...
The audio examples were recorded from a professional Carnatic percussionist in a semi-anechoic studi...
The Mridangam Stroke dataset is a collection of 7162 audio examples of individual strokes of the Mri...
The mridangam is a double-headed percussion instrument that plays a key role in Carnatic music conce...
Percussion instruments play a significant role in/nCarnatic music concerts. The percussion...
Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Non-negative matrix factorizatio...
Abstract — In this paper, a class of algorithms for automatic classification of individual musical i...
Given a musical audio recording, the goal of music transcription is to determine a score-like repres...
Given a musical audio recording, the goal of music transcription is to determine a score-like repres...
This paper proposes a real-time capable method for transcribing and separating occurrences of single...
Automatic Music Transcription (AMT) is concerned with the problem of producing the pitch content of ...
In this paper, a new approach for automatic audio classification using non-negative matrix factoriza...
International audiencePolyphonic pitch transcription consists of estimating the onset time, duration...
The Mridangam Tani-avarthanam dataset is a transcribed collection of two tani-avarthanams played by ...