Hidden Markov Model based recognition of musical pattern in South Indian Classical Music
Automatic recognition of musical patterns plays a crucial part in Musicological and Ethno musicological research and can become an indispensable tool for the search and comparison of music extracts within a large multimedia database. This paper finds an efficient method for recognizing isolated musical patterns in a monophonic environment, using Hidden Markov Model. Each pattern, to be recognized, is converted into a sequence of frequency jumps by means of a fundamental frequency tracking algorithm, followed by a quantizer. The resulting sequence of frequency jumps is presented to the input of the recognizer which use Hidden Markov Model. The main characteristic of Hidden Markov Model is that it utilizes the stochastic information from the musical frame to recognize the pattern. The methodology is tested in the context of South Indian Classical Music, which exhibits certain characteristics that make the classification task harder, when compared with Western musical tradition. Recognition of 100% has been obtained for the six typical music pattern used in practise. South Indian classical instrument, flute is used for the whole experiment.
Summary
This paper presents a practical pipeline for recognizing isolated musical patterns in monophonic South Indian classical music using Hidden Markov Models (HMMs). It explains how to extract pitch-based features by tracking the fundamental frequency, quantizing frequency jumps into symbolic sequences, and using HMM training/decoding to perform pattern recognition and retrieval.
Key Takeaways
- Implement a pitch-tracking front end to extract fundamental frequency (F0) contours from monophonic music.
- Convert continuous F0 contours into a sequence of quantized frequency-jump symbols suitable for sequence modeling.
- Design and train HMMs (including topology and parameter estimation) for isolated-pattern recognition using Baum–Welch and Viterbi algorithms.
- Evaluate recognition performance and understand practical considerations for monophonic music pattern spotting and retrieval.
Who Should Read This
Researchers and engineers with some DSP and ML experience working on music information retrieval, audio/speech processing, or ethnomusicology who need practical methods for pitch-based pattern recognition.
Still RelevantIntermediate
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