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Audio Source Separation for Music in Low-Latency and High-Latency Scenarios Audio Source Separation for Music in Low-latency and High-latency Scenarios Ricard Marxer Pi~n´on TESI DOCTORAL UPF / 2013 Directors de la tesi Dr. Xavier Serra i Dr. Jordi Janer Department of Information and Communication Technologies This dissertation is dedicated to my family and loved ones. Acknowledgments I wish to thank the Music Technology Group (MTG) at the Universitat Pompeu Fabra (UPF) for creating such a great environment in which to work on this research. I especially want to thank my supervisors Xavier Serra and Jordi Janer for giving me this opportunity and their support during the whole process. I also want to express my deepest gratitude to the Yamaha Corporation and their Monet research team formed by Keita Arimoto, Sean Hashimoto, Kazunobu Kondo, Yu Takahashi and Yasuyuki Umeyama, without whom this work would not have been possible. I also want to thank MTG's signal processing team composed of Merlijn Blaauw, Jordi Bonada, Graham Coleman, Saso Musevic and Marti Umbert with whom we had many fruitful discussions that became the seeds of the research conducted here. Another special thanks goes to the Music Cognition Group at the ILLC / University of Amsterdam with Henkjan Honing, Leigh Smith and Olivia Ladinig who invited me to stay and do research with them for a while. There I learned a lot about how we humans perceive music and how this can be taken into account when processing these types of signals. Many thanks to all the researchers with whom I have collaborated, discus- sed and shared great moments over these years. Some of these are Eduard Aylon, Andreas Beisler, Dmitry Bogdanov, Oscar` Celma, Maarten de Boer, Juan Jos´eBosch, Ferdinand Fuhrmann, Jordi Funollet, Cristina Garrido, Emilia G´omez,Perfecto Herrera, Piotr Holonowicz, Enric Guaus, Mart´ın Haro, Cyril Laurier, Oscar Mayor, Owen Meyers, Waldo Nogueira, Hen- drik Purwins, Gerard Roma, Justin Salamon, Joan Serr`a,Mohamed Sordo, Roberto Toscano, Nicolas Wack and Jos´eR. Zapata. I'm sorry if I have v vi forgotten anyone of you who have aided me so much. Many thanks to my father, Jack Marxer, for his valuable feedback, proo- freading and corrections of this document. Finally I would like to thank my family and Mathilde for being there when I most needed them. Abstract Source separation in digital signal processing consists in finding the original signals that were mixed together into a set of observed signals. Solutions to this problem have been extensively studied for musical signals, however their application to real-world practical situations remains infrequent. There are two main obstacles for their widespread adoption depending on the scenario. The main limitation in some cases is high latency and computational cost. In other cases the quality of the results is insufficient. Much work has gone toward improving the quality of music separation under general conditions. But few studies have been devoted to the development of low-latency and low computational cost separation of monaural signals, as well as to the separation quality of specific instruments. We propose specific methods to address these issues in each of these scena- rios independently. First, we focus on methods with low computational cost and low latency. We propose the use of Tikhonov regularization as a method for spectrum decomposition in the low-latency context. We compare it to existing techniques in pitch estimation and tracking tasks, crucial steps in many separation methods. We then use the proposed spectrum decompo- sition method in low-latency separation tasks targeting singing voice, bass and drums. Second, we propose several high-latency methods that improve the separation of singing voice by modeling components that are often not accounted for, such as breathiness and consonants. Finally, we explore using temporal correlations and human annotations to enhance the separation of drums and complex polyphonic music signals. vii Resum En el camp del tractament digital de senyal, la separaci´ode fonts consisteix en l'obtenci´odels senyals originals que trobem barrejats en un conjunt de se- nyals observats. Les solucions a aquest problema s'han estudiat `ampliament per a senyals musicals. Hi ha per`odues limitacions principals per a la seva aplicaci´ogeneralitzada. En alguns casos l'alta lat`enciai cost computacional del m`etode ´esl'obstacle principal. En un segon escenari, la qualitat dels resultats ´esinsuficient. Gran part de la recerca s'ha enfocat a la millora de qualitat de separaci´ode la m´usicaen condicions generals, per`opocs es- tudis s'han centrat en el desenvolupament de t`ecniquesde baixa lat`enciai baix cost computacional de mescles monoaurals, aix´ıcom en la qualitat de separaci´ode instruments espec´ıfics. Aquesta tesi proposa m`etodes per tractar aquests temes en cadascun dels dos casos de forma independent. En primer lloc, ens centrem en els m`etodes amb un baix cost computacional i baixa lat`encia. Proposem l'´usde la regularitzaci´ode Tikhonov com a m`etode de descomposici´ode l'espectre en el context de baixa lat`encia. El comparem amb les t`ecniquesexistents en tasques d’estimaci´oi seguiment dels tons, que s´onpassos crucials en molts m`etodes de separaci´o. A continuaci´outilitzem i avaluem el m`etode de descomposici´ode l'espectre en tasques de separaci´ode veu cantada, baix i percussi´o. En segon lloc, proposem diversos m`etodes d'alta lat`enciaque milloren la separaci´ode la veu cantada, gr`aciesal modelatge de components espec´ıfics,com la respiraci´oi les consonants. Finalment, explorem l'´usde correlacions temporals i anotacions manuals per millorar la separaci´odels instruments de percussi´oi dels senyals musicals polif`onicscomplexes. ix Resumen En el campo del tratamiento digital de la se~nal,la separaci´onde fuentes consiste en la obtenci´onde las se~nalesoriginales que han sido mezcladas en un conjunto de se~nalesobservadas. Las soluciones a este problema se han estudiado ampliamente para se~nalesmusicales. Hay dos limitaciones principales para su adopci´ongeneralizada. En algunos casos la alta latencia y coste computacional es el mayor obst´aculo.En un segundo escenario, la calidad de los resultados es insuficiente. Gran parte de la investigaci´onse ha enfocado en la mejora de la calidad de separaci´onde la m´usicaen condiciones generales, pero pocos estudios se han centrado en el desarrollo de t´ecnicasde baja latencia y bajo coste computacional de mezclas monoaurales, as´ıcomo en la calidad de separaci´onde instrumentos espec´ıficos. Esta tesis propone m´etodos para tratar estos temas en cada uno de los ca- sos de forma independiente. En primer lugar, nos centramos en los m´etodos con un bajo coste computacional y baja latencia. Proponemos el uso de la regularizaci´onde Tikhonov como m´etodo de descomposici´ondel espectro en el contexto de baja latencia. Lo comparamos con las t´ecnicasexistentes en tareas de estimaci´ony seguimiento de los tonos, que son pasos cruciales en muchos m´etodos de separaci´on.A continuaci´onutilizamos y evaluamos el m´etodo de descomposici´ondel espectro en tareas de separaci´onde voz cantada, bajo y percusi´on.En segundo lugar, proponemos varios m´etodos de alta latencia que mejoran la separaci´onde la voz cantada, gracias al modelado de componentes que a menudo no se toman en cuenta, como la respiraci´ony las consonantes. Finalmente, exploramos el uso de correlacio- nes temporales y anotaciones manuales para mejorar la separaci´onde los instrumentos de percusi´ony se~nalesmusicales polif´onicascomplejas. xi R´esum´e Dans le domaine du traitement du signal, la s´eparationde source consiste `aobtenir les signaux originaux qui ont ´et´em´elang´esdans un ensemble de signaux observ´ees.Les solutions `ace probl`emeont largement ´et´e´etudi´es pour l'application `ala musique. Il existe deux principales limitations. La premi`ereest une latence et un co^utde calcul ´elev´es.La seconde est une qualit´einsuffisante des r´esultats.Une grande partie de la recherche actuelle s'est concentr´ee`aam´eliorer,dans le cas g´en´erale,la qualit´ede s´eparationde la musique. Peu d'´etudes ont ´et´econsacr´ees`aminimiser la latence et le co^ut de calcul des techniques, ainsi qu’`ala qualit´ede s´eparationd'instruments sp´ecifiques. Cette th`esepropose des m´ethodes pour aborder ces deux questions ind´epen- damment. Dans un premier temps, nous nous concentrons sur les m´ethodes `afaibles co^ut de calcul et de latence. Pour cela, nous proposons d'utiliser la r´egularisationde Tikhonov en tant que m´ethode de d´ecomposition spec- trale. Nous la comparons `ades techniques existantes dans le cadre de l'estimation et de suivi des tons, qui sont des ´etapes cruciales pour de nom- breux proc´ed´esde s´eparation.Nous avons aussi utilis´eet ´evalu´ela m´ethode de d´ecomposition spectrale pour la s´eparation de voix chant´ee,de basse et de percussion. Dans un second temps, nous proposons des m´ethodes `ala- tence ´elev´ee.La mod´elisationdes composants qui ne sont souvent pas pris en compte, comme la respiration et les consonnes, nous permet d'am´eliorer la s´eparationde voix chant´ee.Nous explorons l'utilisation de corr´elations temporelles et annotations manuelles pour am´eliorerla s´eparationdes ins- truments de percussion et les signaux musicaux polyphoniques complexes. xiii Contents Acknowledgmentsv Abstract vii Resum ix Resumen xi R´esum´e xiii Contents xv List of Figures xviii List of Tables xxii List of Abbreviations and Symbols xxiii 1 Introduction3 1.1 Applications............................ 5 1.2 Motivation and Objectives.................... 9 1.3 Context of the Study....................... 10 1.4 Presentation of Contributions.................. 10 1.5 Organization ........................... 11 2 Audio Source Separation Basics 15 2.1 Problem Definition and Classification.............
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