Publication

Abstract : 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 monophonie environment, using a preprocessing stage, Tempo-tracker using Kalman Filter and Hidden Markov Model. Each musical clip, to be recognized, is first pre-processed by passing through a bandpass filter and its frequency transition points are estimated using Kaiman Filter. This information is used to convert the signal 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 uses 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. Perfect recognition has been obtained for the eight typical music pattern used in practise. South Indian classical instrument, flute, is used for the whole experiment.