Blind Clustering of Popular Music Recordings Based on Singer Voice Characteristics

This paper presents an effective technique for automatically clustering undocumented music recordings based on their associated singer. This serves as an indispensable step towards indexing and content-based information retrieval of music by singer. The proposed clustering system operates in an unsupervised manner, in which no prior information is available regarding the characteristics of singer voices, nor the population of singers. Methods are presented to separate vocal from non-vocal regions, to isolate the singers' vocal characteristics from the background music, to compare the similarity between singers' voices, and to determine the total number of unique singers from a collection of songs. Experimental evaluations conducted on a 200-track pop music database confirm the validity of the proposed system

The Role of Accent in Popular Music: An Interdisciplinary Approach

In the thirty years that have passed since Peter Trudgill first published his study of British pop-song pronunciation, and fifteen years since Paul Simpson published his follow-up study of accents in pop and rock singing (1999), there have been several changes in the way linguists approach the sociolinguistics of singing. These changes include Franz Andres Morissey's introduction of sonority as a factor behind choosing particular phonological features, and the ongoing and evolving criticism of Trudgill's original assertion that singers were (and possibly still are) trying to 'imitate' Americans. The present study argues that existing theories are insufficient, and proposes a new framework for dealing with phonological choice in song, centred around three separate but unavoidably interrelated values that influence style choice – aesthetic, sonority, and indexicality. Unlike many related studies, it places emphasis on the interdisciplinary nature of the subject, drawing upon the work of musicologists, philosophers and linguists, in an attempt to bring a fresh perspective on the phenomenon. Special attention is given to the notion that singers use accents to create (or be appropriate to) a particular aesthetic. The view is taken that music scenes act as unique speech communities that possess both socially and musically derived linguistic norms that all members accept (both performers and audience), but only few actively utilise in their language use (the singers)

A Highly Robust Audio Monitoring System for Radio Broadcasting

Proposing a novel approach for monitoringsongs for the radio broadcasting channels is veryimportant for the interest of singers, writers andmusicians in the musical industry. Singers, writers andmusicians have a claim to intellectual property rightsfor their songs broadcast over all the radio channels.According to this intellectual property rights actsingers, writers and musicians should be paid for theirsongs broadcast over all the radio channels. Therefore wepropose a real time audio monitoring approach to solvethis problem which includes our own audio recognitionalgorithm. It is easy to recognize a song, when you providethe original high quality blueprint of the song as input. Butwe can’t expect such kind of audio input from radiochannels since lots of transformations are possible beforereaching the end user or listener. For example, addingenvironmental effects such as noise, adding commercialson the song as watermarks, playing more than one songas a chain without adding any silence between them,playing a part of the song, playing same song in variousspeeds and so on. These transformations cause change inthe uniqueness of particular song and make the problemeven more difficult. The algorithm we proposing is resistantto noise and distortion as well as it is capable of recognizingshort segment of song when broadcasting over the radiochannels. At the end of the processing our system generatesa descriptive report including title of the song, singer of thesong, writer of the song, composer of the song, number oftimes it was played and when it was played for all songs fora particular period for all radio broadcasting channels. Weevaluate our system against various types of real timescenarios and achieved overall higher level of accuracy(96%) at the end

Automated analysis of musical structure

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2005.Includes bibliographical references (p. 93-96).Listening to music and perceiving its structure is a fairly easy task for humans, even for listeners without formal musical training. For example, we can notice changes of notes, chords and keys, though we might not be able to name them (segmentation based on tonality and harmonic analysis); we can parse a musical piece into phrases or sections (segmentation based on recurrent structural analysis); we can identify and memorize the main themes or the catchiest parts - hooks - of a piece (summarization based on hook analysis); we can detect the most informative musical parts for making certain judgments (detection of salience for classification). However, building computational models to mimic these processes is a hard problem. Furthermore, the amount of digital music that has been generated and stored has already become unfathomable. How to efficiently store and retrieve the digital content is an important real-world problem. This dissertation presents our research on automatic music segmentation, summarization and classification using a framework combining music cognition, machine learning and signal processing. It will inquire scientifically into the nature of human perception of music, and offer a practical solution to difficult problems of machine intelligence for automatic musical content analysis and pattern discovery.(cont.) Specifically, for segmentation, an HMM-based approach will be used for key change and chord change detection; and a method for detecting the self-similarity property using approximate pattern matching will be presented for recurrent structural analysis. For summarization, we will investigate the locations where the catchiest parts of a musical piece normally appear and develop strategies for automatically generating music thumbnails based on this analysis. For musical salience detection, we will examine methods for weighting the importance of musical segments based on the confidence of classification. Two classification techniques and their definitions of confidence will be explored. The effectiveness of all our methods will be demonstrated by quantitative evaluations and/or human experiments on complex real-world musical stimuli.by Wei Chai.Ph.D

Historical sources of ethnomusicology in contemporary debate

This anthology concerns traditional music and archives, and discusses their relationship as seen from historical and epistemological perspectives. Music recordings on wax cylinders, 78 records or magnetic tape, made in the first half of the 20th century, are regarded today as valuable sources for understanding musical processes in their social dimension and as unique cultural heritage. Most of these historical sound recordings are preserved in sound archives, now increasingly accessible in digital formats. Written by renowned experts, the articles here focus on archives, individual and collective memory, and heritage as today’s recreation of the past. Contributors discuss the role of historical sources of traditional music in contemporary research based on examples from music cultures in West Africa, Scandinavia, Turkey, and Portugal, among others. The book will appeal to musicologists and cultural anthropologists, as well as historians and sociologists, and will be of interest to anyone concerned with sound archives, libraries, universities and cultural institutions dedicated to traditional music

Kompozicionalni hierarhični model za pridobivanje informacij iz glasbe

In recent years, deep architectures, most commonly based on neural networks, have advanced the state of the art in many research areas. Due to the popularity and the success of deep neural-networks, other deep architectures, including compositional models, have been put aside from mainstream research. This dissertation presents the compositional hierarchical model as a novel deep architecture for music processing. Our main motivation was to develop and explore an alternative non-neural deep architecture for music processing which would be transparent, meaning that the encoded knowledge would be interpretable, trained in an unsupervised manner and on small datasets, and useful as a feature extractor for classification tasks, as well as a transparent model for unsupervised pattern discovery. We base our work on compositional models, as compositionality is inherent in music. The proposed compositional hierarchical model learns a multi-layer hierarchical representation of the analyzed music signals in an unsupervised manner. It provides transparent insights into the learned concepts and their structure. It can be used as a feature extractor---its output can be used for classification tasks using existing machine learning techniques. Moreover, the model\u27s transparency enables an interpretation of the learned concepts, so the model can be used for analysis (exploration of the learned hierarchy) or discovery-oriented (inferring the hierarchy) tasks, which is difficult with most neural network based architectures. The proposed model uses relative coding of the learned concepts, which eliminates the need for large annotated training datasets that are essential in deep architectures with a large number of parameters. Relative coding contributes to slim models, which are fast to execute and have low memory requirements. The model also incorporates several biologically-inspired mechanisms that are modeled according to the mechanisms that exists at the lower levels of human perception (eg~ lateral inhibition in the human ear) and that significantly affect perception. The proposed model is evaluated on several music information retrieval tasks and its results are compared to the current state of the art. The dissertation is structured as follows. In the first chapter we present the motivation for the development of the new model. In the second chapter we elaborate on the related work in music information retrieval and review other compositional and transparent models. Chapter three introduces a thorough description of the proposed model. The model structure, its learning and inference methods are explained, as well as the incorporated biologically-inspired mechanisms. The model is then applied to several different music domains, which are divided according to the type of input data. In this we follow the timeline of the development and the implementation of the model. In chapter four, we present the model\u27s application to audio recordings, specifically for two tasks: automatic chord estimation and multiple fundamental frequency estimation. In chapter five, we present the model\u27s application to symbolic music representations. We concentrate on pattern discovery, emphasizing the model\u27s ability to tackle such problems. We also evaluate the model as a feature generator for tune family classification. Finally, in chapter six, we show the latest progress in developing the model for representing rhythm and show that it exhibits a high degree of robustness in extracting high-level rhythmic structures from music signals. We conclude the dissertation by summarizing our work and the results, elaborating on forthcoming work in the development of the model and its future applications.S porastom globokih arhitektur, ki temeljijo na nevronskih mrežah, so se v zadnjem času bistveno izboljšali rezultati pri reševanju problemov na več področjih. Zaradi popularnosti in uspešnosti teh globokih pristopov, temelječih na nevronskih mrežah, so bili drugi, predvsem kompozicionalni pristopi, odmaknjeni od središča pozornosti raziskav. V pričujoči disertaciji se posvečamo vprašanju, ali je mogoče razviti globoko arhitekturo, ki bo presegla obstoječe probleme globokih arhitektur. S tem namenom se vračamo h kompozicionalnim modelom in predstavimo kompozicionalni hierarhični model kot alternativno globoko arhitekturo, ki bo imela naslednje značilnosti: transparentnost, ki omogoča enostavno razlago naučenih konceptov, nenadzorovano učenje in zmožnost učenja na majhnih podatkovnih bazah, uporabnost modela kot izluščevalca značilk, kot tudi zmožnost uporabe transparentnosti modela za odkrivanje vzorcev. Naše delo temelji na kompozicionalnih modelih, ki so v glasbi intuitivni. Predlagani kompozicionalni hierarhični model je zmožen nenadzorovanega učenja večnivojske predstavitve glasbenega vhoda. Model omogoča pregled naučenih konceptov skozi transparentne strukture. Lahko ga uporabimo kot generator značilk -- izhod modela lahko uporabimo za klasifikacijo z drugimi pristopi strojnega učenja. Hkrati pa lahko transparentnost predlaganega modela uporabimo za analizo (raziskovanje naučene hierarhije) pri odkrivanju vzorcev, kar je težko izvedljivo z ostalimi pristopi, ki temeljijo na nevronskih mrežah. Relativno kodiranje konceptov v samem modelu pripomore k precej manjšim modelom in posledično zmanjšuje potrebo po velikih podatkovnih zbirkah, potrebnih za učenje modela. Z vpeljavo biološko navdahnjenih mehanizmov želimo model še bolj približati človeškemu načinu zaznave. Za nekatere mehanizme, na primer inhibicijo, vemo, da so v človeški percepciji prisotni na nižjih nivojih v ušesu in bistveno vplivajo na način zaznave. V modelu uvedemo prve korake k takšnemu načinu procesiranja proti končnemu cilju izdelave modela, ki popolnoma odraža človeško percepcijo. V prvem poglavju disertacije predstavimo motivacijo za razvoj novega modela. V drugem poglavju se posvetimo dosedanjim objavljenim dosežkom na tem področju. V nadaljnjih poglavjih se osredotočimo na sam model. Sprva opišemo teoretično zasnovo modela in način učenja ter delovanje biološko-navdahnjenih mehanizmov. V naslednjem koraku model apliciramo na več različnih glasbenih domen, ki so razdeljene glede na tip vhodnih podatkov. Pri tem sledimo časovnici razvoja in implementacijam modela tekom doktorskega študija. Najprej predstavimo aplikacijo modela za časovno-frekvenčne signale, na katerem model preizkusimo za dve opravili: avtomatsko ocenjevanje harmonij in avtomatsko transkripcijo osnovnih frekvenc. V petem poglavju predstavimo drug način aplikacije modela, tokrat na simbolne vhodne podatke, ki predstavljajo glasbeni zapis. Pri tem pristopu se osredotočamo na odkrivanje vzorcev, s čimer poudarimo zmožnost modela za reševanje tovrstnih problemov, ki je ostalim pristopom še nedosegljivo. Model prav tako evalviramo v vlogi generatorja značilk. Pri tem ga evalviramo na problemu melodične podobnosti pesmi in razvrščanja v variantne tipe. Nazadnje, v šestem poglavju, pokažemo zadnji dosežek razvoja modela, ki ga apliciramo na problem razumevanja ritma v glasbi. Prilagojeni model analiziramo in pokažemo njegovo zmožnost učenja različnih ritmičnih oblik in visoko stopnjo robustnosti pri izluščevanju visokonivojskih struktur v ritmu. V zaključkih disertacije povzamemo vloženo delo in rezultate ter nakažemo nadaljnje korake za razvoj modela v prihodnosti

Signal processing methods for beat tracking, music segmentation, and audio retrieval

The goal of music information retrieval (MIR) is to develop novel strategies and techniques for organizing, exploring, accessing, and understanding music data in an efficient manner. The conversion of waveform-based audio data into semantically meaningful feature representations by the use of digital signal processing techniques is at the center of MIR and constitutes a difficult field of research because of the complexity and diversity of music signals. In this thesis, we introduce novel signal processing methods that allow for extracting musically meaningful information from audio signals. As main strategy, we exploit musical knowledge about the signals\u27 properties to derive feature representations that show a significant degree of robustness against musical variations but still exhibit a high musical expressiveness. We apply this general strategy to three different areas of MIR: Firstly, we introduce novel techniques for extracting tempo and beat information, where we particularly consider challenging music with changing tempo and soft note onsets. Secondly, we present novel algorithms for the automated segmentation and analysis of folk song field recordings, where one has to cope with significant fluctuations in intonation and tempo as well as recording artifacts. Thirdly, we explore a cross-version approach to content-based music retrieval based on the query-by-example paradigm. In all three areas, we focus on application scenarios where strong musical variations make the extraction of musically meaningful information a challenging task.Ziel der automatisierten Musikverarbeitung ist die Entwicklung neuer Strategien und Techniken zur effizienten Organisation großer Musiksammlungen. Ein Schwerpunkt liegt in der Anwendung von Methoden der digitalen Signalverarbeitung zur Umwandlung von Audiosignalen in musikalisch aussagekräftige Merkmalsdarstellungen. Große Herausforderungen bei dieser Aufgabe ergeben sich aus der Komplexität und Vielschichtigkeit der Musiksignale. In dieser Arbeit werden neuartige Methoden vorgestellt, mit deren Hilfe musikalisch interpretierbare Information aus Musiksignalen extrahiert werden kann. Hierbei besteht eine grundlegende Strategie in der konsequenten Ausnutzung musikalischen Vorwissens, um Merkmalsdarstellungen abzuleiten die zum einen ein hohes Maß an Robustheit gegenüber musikalischen Variationen und zum anderen eine hohe musikalische Ausdruckskraft besitzen. Dieses Prinzip wenden wir auf drei verschieden Aufgabenstellungen an: Erstens stellen wir neuartige Ansätze zur Extraktion von Tempo- und Beat-Information aus Audiosignalen vor, die insbesondere auf anspruchsvolle Szenarien mit wechselnden Tempo und weichen Notenanfängen angewendet werden. Zweitens tragen wir mit neuartigen Algorithmen zur Segmentierung und Analyse von Feldaufnahmen von Volksliedern unter Vorliegen großer Intonationsschwankungen bei. Drittens entwickeln wir effiziente Verfahren zur inhaltsbasierten Suche in großen Datenbeständen mit dem Ziel, verschiedene Interpretationen eines Musikstückes zu detektieren. In allen betrachteten Szenarien richten wir unser Augenmerk insbesondere auf die Fälle in denen auf Grund erheblicher musikalischer Variationen die Extraktion musikalisch aussagekräftiger Informationen eine große Herausforderung darstellt