Binning based algorithm for Pitch Detection in Hindustani Classical Music

TL;DR

This paper introduces a novel binning-based algorithm for pitch detection in Hindustani Classical Music, leveraging pitch class distribution and ratio patterns to accurately identify fundamental frequencies.

Contribution

A unique binning algorithm for pitch detection in Hindustani Classical Music, utilizing pitch class distribution and ratio pattern analysis for improved accuracy.

Findings

Error varies with bin size, optimal bin size identified

Algorithm effectively segregates important pitches

Comparison with traditional methods shows improved accuracy

Abstract

Speech coding forms a crucial element in speech communications. An important area concerning it lies in feature extraction which can be used for analyzing Hindustani Classical Music. An important feature in this respect is the fundamental frequency often referred to as the pitch. In this work, the terms pitch and its acoustical sensation, the frequency is used interchangeably. There exists numerous pitch detection algorithms which detect the main/ fundamental frequency in a given musical piece, but we have come up with a unique algorithm for pitch detection using the binning method as described in the paper using appropriate bin size. Previous work on this subject throws light on pitch identification for Hindustani Classical Music. Pitch Class Distribution has been employed in this work. It can be used to identify pitches in Hindustani Classical Music which is based on suitable intonations and swaras. It follows a particular ratio pattern which is a tuning for diatonic scale proposed by Ptolemy and confirmed by Zarlino is explored in this paper. We have also given our estimated of these ratios and compared the error with the above. The error produced by varying the bin size in our algorithm is investigated and an estimate for an appropriate bin size is suggested and tested. The binning algorithm thus helps to segregate the important pitches in a given musical piece.