In this work, we explore the task of musical onset detection in Carnatic music by choosing five major percussion instruments: the mridangam, ghatam, kanjira, morsing and thavil. We explore the musical characteristics of the strokes for each of the above instruments, motivating the challenge in designing an onset detection algorithm. We propose a non-model based algorithm using the minimum-phase group delay for this task. The music signal is treated as an Amplitude-Frequency modulated (AM-FM) waveform, and its envelope is extracted using the Hilbert transform. Minimum phase group delay processing is then applied to accurately determine the onset locations. The algorithm is tested on a large dataset with both controlled and concert recordings (tani avarthanams). The performance is observed to be the comparable with that of the state-of-the-art technique employing machine learning algorithms. © 2015 IEEE.

Hema Murthy

Department of Computer Science and Engineering

Jilt Sebastian

P A Manoj Kumar

Algorithms

Artificial Intelligence

Frequency modulation

Learning algorithms

Learning systems

Mathematical transformations

Musical instruments

AMPLITUDE FREQUENCY

Envelope detection

GROUP DELAY FUNCTIONS

Hilbert transform

MODEL-BASED ALGORITHMS

Onset detection

PERCUSSION INSTRUMENTS

State-of-the-art techniques

Group delay

IIT Madras is a public technical and research university located in Chennai, Tamil Nadu. Founded in 1959, it is recognised as an Institute of National Importance.

IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

It currently offers undergraduate, postgraduate and research degrees across 16 disciplines in Engineering, Sciences, Humanities and Management. About 596 faculty belonging to science and engineering departments and centres of the Institute are engaged in teaching, research and industrial consultancy.

IIT Madras

Musical onset detection on carnatic percussion instruments

2015 21st National Conference on Communications, NCC 2015

HMM based speech synthesis (HTS) is a state-of-the art approach to text-to-speech synthesis. Segmentation of the training data is essential for building any text-to-speech system. Most conventional text-to-speech systems use phones as the basic unit of synthesis and use a speech recogniser to automatically segment the data at the phone level. As Indian languages are low resource languages, accurate transcriptions are difficult to obtain owing to paucity of data. Manual labeling at the phone level is not only laborious but also inaccurate. HMM based flat start segmentation doesn't work well at the sentence level. In this paper we propose an event driven approach to obtain better phone boundaries. Syllable-like events are detected in the speech signal and matched with syllabified transcription of the text. The syllables are converted to phoneme sequences and Baum-Welch embedded re-estimation is restricted to the syllable-level. Subjective evaluations indicate that the proposed system has a lower word error rate compared to that of a conventional system that uses flat start for obtaining phone boundaries. © 2014 IEEE.

2014 20th National Conference on Communications, NCC 2014

Group delay based phone segmentation for HTS

In this paper we use a Non-negative Matrix Factorization (NMF) based approach to analyze the strokes of the mridangam, a South Indian hand drum, in terms of the normal modes of the instrument. Using NMF, a dictionary of spectral basis vectors are first created for each of the modes of the mridangam. The composition of the strokes are then studied by projecting them along the direction of the modes using NMF. We then extend this knowledge of each stroke in terms of its basic modes to transcribe audio recordings. Hidden Markov Models are adopted to learn the modal activations for each of the strokes of the mridangam, yielding up to 88.40% accuracy during transcription. © 2013 IEEE.

Fulltext

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Modal analysis and transcription of strokes of the mridangam using non-negative matrix factorization

2014 International Joint Conference on Neural Networks (IJCNN)

Audio onset detection: A wavelet packet based approach with recurrent neural networks

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Multi-resolution linear prediction based features for audio onset detection with bidirectional LSTM neural networks

Improved musical onset detection with Convolutional Neural Networks

Journal	Data powered by Typeset2015 21st National Conference on Communications, NCC 2015
Publisher	Data powered by TypesetInstitute of Electrical and Electronics Engineers Inc.
Open Access	No