A Robust Audio Fingerprinter Based on Pitch Class Histograms Applications for Ethnic Music Archives

In this paper we present a new acoustic fingerprinting system, based on pitch class histograms. The aim of acoustic fingerprinting is to generate a small representation of an audio signal that can be used to identify identical, or recognize similar, audio snippets in a large audio set. A robust fingerprinting system generates similar fingerprints for perceptually similar audio signals. A piece of music with a noise added should generate an almost identical fingerprint as the original. The new system, presented here, has some interesting features which makes it a valuable tool to manage ethnic music archives: the fingerprints are rather robust against pitch shift, tempo changes, several synthetic audio effects, and reversal of the audio. When only part of the audio is used to generate a fingerprint, the system keeps working but retrieval performance degrades

Arabic Music Genre Identification

Published by: Semarak Ilmu Publishing. This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial 4.0 International License (CC BY-NC), https://creativecommons.org/licenses/by-nc/4.0/Music Information Retrieval (MIR) is one data science application crucial for different tasks such as recommendation systems, genre identification, fingerprinting, and novelty assessment. Different Machine Learning techniques are utilised to analyse digital music records, such as clustering, classification, similarity scoring, and identifying various properties for the different tasks. Music is represented digitally using diverse transformations and is clustered and classified successfully for Western Music. However, Eastern Music poses a challenge, and some techniques have achieved success in clustering and classifying Turkish and Persian Music. This research presents an evaluation of machine learning algorithms' performance on pre-labelled Arabic Music with their Arabic genre (Maqam). The study introduced new data representations of the Arabic music dataset and identified the most suitable machine-learning methods and future enhancements.Peer reviewe

Temporal Evolution of Makam and Usul Relationship in Turkish Makam

Turkish makam music is transmitted orally and learned through repetition. Most previous computational analysis works focus either on makam (its melodic structure) or usul (its rhythmic pattern) separately. The work presented in this paper performs a combined analysis to explore the descriptive potential of the relationship between these in over 600 makam pieces

Automatic transcription of traditional Turkish art music recordings: A computational ethnomusicology appraoach

Thesis (Doctoral)--Izmir Institute of Technology, Electronics and Communication Engineering, Izmir, 2012Includes bibliographical references (leaves: 96-109)Text in English; Abstract: Turkish and Englishxi, 131 leavesMusic Information Retrieval (MIR) is a recent research field, as an outcome of the revolutionary change in the distribution of, and access to the music recordings. Although MIR research already covers a wide range of applications, MIR methods are primarily developed for western music. Since the most important dimensions of music are fundamentally different in western and non-western musics, developing MIR methods for non-western musics is a challenging task. On the other hand, the discipline of ethnomusicology supplies some useful insights for the computational studies on nonwestern musics. Therefore, this thesis overcomes this challenging task within the framework of computational ethnomusicology, a new emerging interdisciplinary research domain. As a result, the main contribution of this study is the development of an automatic transcription system for traditional Turkish art music (Turkish music) for the first time in the literature. In order to develop such system for Turkish music, several subjects are also studied for the first time in the literature which constitute other contributions of the thesis: Automatic music transcription problem is considered from the perspective of ethnomusicology, an automatic makam recognition system is developed and the scale theory of Turkish music is evaluated computationally for nine makamlar in order to understand whether it can be used for makam detection. Furthermore, there is a wide geographical region such as Middle-East, North Africa and Asia sharing similarities with Turkish music. Therefore our study would also provide more relevant techniques and methods than the MIR literature for the study of these non-western musics

A computational analysis of Turkish makam music based on a probabilistic characterization of segmented phrases

This study targets automatic analysis of Turkish makam music pieces on the phrase level. While makam is most simply defined as an organization of melodic phrases, there has been very little effort to computationally study melodic structure in makam music pieces. In this work, we propose an automatic analysis algorithm that takes as input symbolic data in the form of machine-readable scores that are segmented into phrases. Using a measure of makam membership for phrases, our method outputs for each phrase the most likely makam the phrase comes from. The proposed makam membership definition is based on Bayesian classification and the algorithm is specifically designed to process the data with overlapping classes. The proposed analysis system is trained and tested on a large data set of phrases obtained by transferring phrase boundaries manually written by experts of makam music on printed scores, to machine-readable data. For the task of classifying all phrases, or only the beginning phrases to come from the main makam of the piece, the corresponding F-measures are.52 and.60 respectively.Scientific and Technological Research Council of Turkey, TUBITAK (112E162

"Yarman-36 makam tone-system" for Turkish art music

This study offers a mathematically rigorous, yet straightforward, fixed-pitch tuning strategy to the problem of adequate sounding and notating of essential Turkish makam genera, in contradistinction to the praxis-mismatched music theory cast in effect known as Arel-Ezgi-Uzdilek (AEU). It comprises 36 tones locatable just by ear, via counting exact 0, 1 and 2 beats per second when listening to given octave, fifth and third intervals, starting from an algebraically attained reference frequency for A at 438.41 Hertz, very near the international standard A=440 Hz. The so-named Yarman-36 makam tone-system proposed in this paper accounts for hitherto omitted pitches in Uşşak, Saba, Hüzzam, etc... at popular transpositions, each corresponding to a habitually used Ahenk (concert pitch level specified by a chosen Ney reed), by virtue of being based on a twelve-by-twelve triplex structure of exclusively tailored Modified Meantone Baroque Temperaments. It thus also features pleasant shades of key-colors supporting polyphonic endeavours in line with Western common practice music.Publisher's Versio

The Pitch Histogram of Traditional Chinese Anhemitonic Pentatonic Folk Songs

Funding Information: The APC was funded by Open Access Initiative of the University of Bremen and the DFG via SuUB Bremen. Publisher Copyright: © 2022 by the authors.As an essential subset of Chinese music, traditional Chinese folk songs frequently apply the anhemitonic pentatonic scale. In music education and demonstration, the Chinese anhemitonic pentatonic mode is usually introduced theoretically, supplemented by music appreciation, and a non-Chinese-speaking audience often lacks a perceptual understanding. We discovered that traditional Chinese anhemitonic pentatonic folk songs could be identified intuitively according to their distinctive bell-shaped pitch distribution in different types of pitch histograms, reflecting the Chinese characteristics of Zhongyong (the doctrine of the mean). Applying pitch distribution to the demonstration of the Chinese anhemitonic pentatonic folk songs, exemplified by a considerable number of instances, allows the audience to understand the culture behind the music from a new perspective by creating an auditory and visual association. We have also made preliminary attempts to feature and model the observations and implemented pilot classifiers to provide references for machine learning in music information retrieval (MIR). To the best of our knowledge, this article is the first MIR study to use various pitch histograms on traditional Chinese anhemitonic pentatonic folk songs, demonstrating that, based on cultural understanding, lightweight statistical approaches can progress cultural diversity in music education, computational musicology, and MIR.publishersversionpublishe

On the Distributional Representation of Ragas: Experiments with Allied Raga Pairs

Raga grammar provides a theoretical framework that supports creativity and flexibility in improvisation while carefully maintaining the distinctiveness of each raga in the ears of a listener. A computational model for raga grammar can serve as a powerful tool to characterize grammaticality in performance. Like in other forms of tonal music, a distributional representation capturing tonal hierarchy has been found to be useful in characterizing a raga’s distinctiveness in performance. In the continuous-pitch melodic tradition, several choices arise for the defining attributes of a histogram representation of pitches. These can be resolved by referring to one of the main functions of the representation, namely to embody the raga grammar and therefore the technical boundary of a raga in performance. Based on the analyses of a representative dataset of audio performances in allied ragas by eminent Hindustani vocalists, we propose a computational representation of distributional information, and further apply it to obtain insights about how this aspect of raga distinctiveness is manifested in practice over different time scales by very creative performers

Pitch-frequency histogram-based music information retrieval for Turkish music

This study reviews the use of pitch histograms in music information retrieval studies for western and non-western music. The problems in applying the pitch-class histogram-based methods developed for western music to non-western music and specifically to Turkish music are discussed in detail. The main problems are the assumptions used to reduce the dimension of the pitch histogram space, such as, mapping to a low and fixed dimensional pitch-class space, the hard-coded use of western music theory, the use of the standard diapason (A4=440 Hz), analysis based on tonality and tempered tuning. We argue that it is more appropriate to use higher dimensional pitch-frequency histograms without such assumptions for Turkish music. We show in two applications, automatic tonic detection and makam recognition, that high dimensional pitch-frequency histogram representations can be successfully used in Music Information Retrieval (MIR) applications without such pre-assumptions, using the data-driven models. © 2009 Elsevier B.V. All rights reserved.TÜBİTAK (Project no:107E024