고유 특성을 활용한 음악에서의 보컬 분리

학위논문 (박사)-- 서울대학교 대학원 : 융합과학기술대학원 융합과학부, 2018. 2. 이교구.보컬 분리란 음악 신호를 보컬 성분과 반주 성분으로 분리하는 일 또는 그 방법을 의미한다. 이러한 기술은 음악의 특정한 성분에 담겨 있는 정보를 추출하기 위한 전처리 과정에서부터, 보컬 연습과 같이 분리 음원 자체를 활용하는 등의 다양한 목적으로 사용될 수 있다. 본 논문의 목적은 보컬과 반주가 가지고 있는 고유한 특성에 대해 논의하고 그것을 활용하여 보컬 분리 알고리즘들을 개발하는 것이며, 특히 `특징 기반' 이라고 불리는 다음과 같은 상황에 대해 중점적으로 논의한다. 우선 분리 대상이 되는 음악 신호는 단채널로 제공된다고 가정하며, 이 경우 신호의 공간적 정보를 활용할 수 있는 다채널 환경에 비해 더욱 어려운 환경이라고 볼 수 있다. 또한 기계 학습 방법으로 데이터로부터 각 음원의 모델을 추정하는 방법을 배제하며, 대신 저차원의 특성들로부터 모델을 유도하여 이를 목표 함수에 반영하는 방법을 시도한다. 마지막으로, 가사, 악보, 사용자의 안내 등과 같은 외부의 정보 역시 제공되지 않는다고 가정한다. 그러나 보컬 분리의 경우 암묵 음원 분리 문제와는 달리 분리하고자 하는 음원이 각각 보컬과 반주에 해당한다는 최소한의 정보는 제공되므로 각각의 성질들에 대한 분석은 가능하다. 크게 세 종류의 특성이 본 논문에서 중점적으로 논의된다. 우선 연속성의 경우 주파수 또는 시간 측면으로 각각 논의될 수 있는데, 주파수축 연속성의 경우 소리의 음색적 특성을, 시간축 연속성은 소리가 안정적으로 지속되는 정도를 각각 나타낸다고 볼 수 있다. 또한, 저행렬계수 특성은 신호의 구조적 성질을 반영하며 해당 신호가 낮은 행렬계수를 가지는 형태로 표현될 수 있는지를 나타내며, 성김 특성은 신호의 분포 형태가 얼마나 성기거나 조밀한지를 나타낸다. 본 논문에서는 크게 두 가지의 보컬 분리 방법에 대해 논의한다. 첫 번째 방법은 연속성과 성김 특성에 기반을 두고 화성 악기-타악기 분리 방법 (harmonic-percussive sound separation, HPSS) 을 확장하는 방법이다. 기존의 방법이 두 번의 HPSS 과정을 통해 보컬을 분리하는 것에 비해 제안하는 방법은 성긴 잔여 성분을 추가해 한 번의 보컬 분리 과정만을 사용한다. 논의되는 다른 방법은 저행렬계수 특성과 성김 특성을 활용하는 것으로, 반주가 저행렬계수 모델로 표현될 수 있는 반면 보컬은 성긴 분포를 가진다는 가정에 기반을 둔다. 이러한 성분들을 분리하기 위해 강인한 주성분 분석 (robust principal component analysis, RPCA) 을 이용하는 방법이 대표적이다. 본 논문에서는 보컬 분리 성능에 초점을 두고 RPCA 알고리즘을 일반화하거나 확장하는 방식에 대해 논의하며, 트레이스 노름과 l1 노름을 각각 샤텐 p 노름과 lp 노름으로 대체하는 방법, 스케일 압축 방법, 주파수 분포 특성을 반영하는 방법 등을 포함한다. 제안하는 알고리즘들은 다양한 데이터셋과 대회에서 평가되었으며 최신의 보컬 분리 알고리즘들보다 더 우수하거나 비슷한 결과를 보였다.Singing voice separation (SVS) refers to the task or the method of decomposing music signal into singing voice and its accompanying instruments. It has various uses, from the preprocessing step, to extract the musical features implied in the target source, to applications for itself such as vocal training. This thesis aims to discover the common properties of singing voice and accompaniment, and apply it to advance the state-of-the-art SVS algorithms. In particular, the separation approach as follows, which is named `characteristics-based,' is concentrated in this thesis. First, the music signal is assumed to be provided in monaural, or as a single-channel recording. It is more difficult condition compared to multiple-channel recording since spatial information cannot be applied in the separation procedure. This thesis also focuses on unsupervised approach, that does not use machine learning technique to estimate the source model from the training data. The models are instead derived based on the low-level characteristics and applied to the objective function. Finally, no external information such as lyrics, score, or user guide is provided. Unlike blind source separation problems, however, the classes of the target sources, singing voice and accompaniment, are known in SVS problem, and it allows to estimate those respective properties. Three different characteristics are primarily discussed in this thesis. Continuity, in the spectral or temporal dimension, refers the smoothness of the source in the particular aspect. The spectral continuity is related with the timbre, while the temporal continuity represents the stability of sounds. On the other hand, the low-rankness refers how the signal is well-structured and can be represented as a low-rank data, and the sparsity represents how rarely the sounds in signals occur in time and frequency. This thesis discusses two SVS approaches using above characteristics. First one is based on the continuity and sparsity, which extends the harmonic-percussive sound separation (HPSS). While the conventional algorithm separates singing voice by using a two-stage HPSS, the proposed one has a single stage procedure but with an additional sparse residual term in the objective function. Another SVS approach is based on the low-rankness and sparsity. Assuming that accompaniment can be represented as a low-rank model, whereas singing voice has a sparse distribution, conventional algorithm decomposes the sources by using robust principal component analysis (RPCA). In this thesis, generalization or extension of RPCA especially for SVS is discussed, including the use of Schatten p-/lp-norm, scale compression, and spectral distribution. The presented algorithms are evaluated using various datasets and challenges and achieved the better comparable results compared to the state-of-the-art algorithms.Chapter 1 Introduction 1 1.1 Motivation 4 1.2 Applications 5 1.3 Definitions and keywords 6 1.4 Evaluation criteria 7 1.5 Topics of interest 11 1.6 Outline of the thesis 13 Chapter 2 Background 15 2.1 Spectrogram-domain separation framework 15 2.2 Approaches for singing voice separation 19 2.2.1 Characteristics-based approach 20 2.2.2 Spatial approach 21 2.2.3 Machine learning-based approach 22 2.2.4 informed approach 23 2.3 Datasets and challenges 25 2.3.1 Datasets 25 2.3.2 Challenges 26 Chapter 3 Characteristics of music sources 28 3.1 Introduction 28 3.2 Spectral/temporal continuity 29 3.2.1 Continuity of a spectrogram 29 3.2.2 Continuity of musical sources 30 3.3 Low-rankness 31 3.3.1 Low-rankness of a spectrogram 31 3.3.2 Low-rankness of musical sources 33 3.4 Sparsity 34 3.4.1 Sparsity of a spectrogram 34 3.4.2 Sparsity of musical sources 36 3.5 Experiments 38 3.6 Summary 39 Chapter 4 Singing voice separation using continuity and sparsity 43 4.1 Introduction 43 4.2 SVS using two-stage HPSS 45 4.2.1 Harmonic-percussive sound separation 45 4.2.2 SVS using two-stage HPSS 46 4.3 Proposed algorithm 48 4.4 Experimental evaluation 52 4.4.1 MIR-1k Dataset 52 4.4.2 Beach boys Dataset 55 4.4.3 iKala dataset in MIREX 2014 56 4.5 Conclusion 58 Chapter 5 Singing voice separation using low-rankness and sparsity 61 5.1 Introduction 61 5.2 SVS using robust principal component analysis 63 5.2.1 Robust principal component analysis 63 5.2.2 Optimization for RPCA using augmented Lagrangian multiplier method 63 5.2.3 SVS using RPCA 65 5.3 SVS using generalized RPCA 67 5.3.1 Generalized RPCA using Schatten p- and lp-norm 67 5.3.2 Comparison of pRPCA with robust matrix completion 68 5.3.3 Optimization method of pRPCA 69 5.3.4 Discussion of the normalization factor for λ 69 5.3.5 Generalized RPCA using scale compression 71 5.3.6 Experimental results 72 5.4 SVS using RPCA and spectral distribution 73 5.4.1 RPCA with weighted l1-norm 73 5.4.2 Proposed method: SVS using wRPCA 74 5.4.3 Experimental results using DSD100 dataset 78 5.4.4 Comparison with state-of-the-arts in SiSEC 2016 79 5.4.5 Discussion 85 5.5 Summary 86 Chapter 6 Conclusion and Future Work 88 6.1 Conclusion 88 6.2 Contributions 89 6.3 Future work 91 6.3.1 Discovering various characteristics for SVS 91 6.3.2 Expanding to other SVS approaches 92 6.3.3 Applying the characteristics for deep learning models 92 Bibliography 94 초 록 110Docto

Audio source separation for music in low-latency and high-latency scenarios

Aquesta tesi proposa mètodes per tractar les limitacions de les tècniques existents de separació de fonts musicals en condicions de baixa i alta latència. En primer lloc, ens centrem en els mètodes amb un baix cost computacional i baixa latència. Proposem l'ús de la regularització de Tikhonov com a mètode de descomposició de l'espectre en el context de baixa latència. El comparem amb les tècniques existents en tasques d'estimació i seguiment dels tons, que són passos crucials en molts mètodes de separació. A continuació utilitzem i avaluem el mètode de descomposició de l'espectre en tasques de separació de veu cantada, baix i percussió. En segon lloc, proposem diversos mètodes d'alta latència que milloren la separació de la veu cantada, gràcies al modelatge de components específics, com la respiració i les consonants. Finalment, explorem l'ús de correlacions temporals i anotacions manuals per millorar la separació dels instruments de percussió i dels senyals musicals polifònics complexes.Esta tesis propone métodos para tratar las limitaciones de las técnicas existentes de separación de fuentes musicales en condiciones de baja y alta latencia. En primer lugar, nos centramos en los métodos con un bajo coste computacional y baja latencia. Proponemos el uso de la regularización de Tikhonov como método de descomposición del espectro en el contexto de baja latencia. Lo comparamos con las técnicas existentes en tareas de estimación y seguimiento de los tonos, que son pasos cruciales en muchos métodos de separación. A continuación utilizamos y evaluamos el método de descomposición del espectro en tareas de separación de voz cantada, bajo y percusión. En segundo lugar, proponemos varios métodos de alta latencia que mejoran la separación de la voz cantada, gracias al modelado de componentes que a menudo no se toman en cuenta, como la respiración y las consonantes. Finalmente, exploramos el uso de correlaciones temporales y anotaciones manuales para mejorar la separación de los instrumentos de percusión y señales musicales polifónicas complejas.This thesis proposes specific methods to address the limitations of current music source separation methods in low-latency and high-latency scenarios. 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 decomposition 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

調波音打楽器音分離による歌声のスペクトルゆらぎに基づく音楽信号処理の研究

学位の種別:課程博士University of Tokyo(東京大学

Shallow and deep learning for audio and natural language processing

Many machine learning algorithms can be viewed as optimization problems that seek the optimum hypothesis in a hypothesis space. To model the complex dependencies in real-world artificial intelligence tasks, machine learning algorithms are required to have high expressive power (high degrees of freedom or richness of a family of functions) and a large amount of training data. Deep learning models and kernel machines are regarded as models with high expressive power through the composition of multiple layers of nonlinearities and through nonlinearly mapping data to a high-dimensional space, respectively. While the majority of deep learning work is focused on pure classification problems given input data, there are many other challenging Artificial Intelligence (AI) problems beyond classification tasks. In real-world applications, there are cases where we have structured relationships between and among input data and output targets, which have not been fully taken into account in deep learning models. On the other hand, though kernel machines involve convex optimization and have strong theoretical grounding in tractable optimization techniques, for large-scale applications, kernel machines often suffer from significant memory requirements and computational expense. Resolving the computational limitation and thereby enhancing the expressibility of kernel machines are important for large-scale real-world applications. Learning models based on deep learning and kernel machines for audio and natural language processing tasks are developed in this dissertation. In particular, we address the challenges for deep learning with structured relationships among data and the computational limitations of large-scale kernel machines. A general framework is proposed to consider the relationship among output predictions and enforce constraints between a mixture input and output predictions for monaural source separation tasks. To model the structured relationships among inputs, the deep structured semantic models are introduced for an information retrieval task. Queries and documents are modeled as inputs to the deep learning models and the relevance is measured through the similarity at the output layer. A discriminative objective function is proposed to exploit the similarity and dissimilarity between queries and web documents. To address the scalability and efficiency of large-scale kernel machines, using deep architectures, ensemble models, and a scalable parallel solver are investigated to further scale-up kernel machines approximated by randomized feature maps. The proposed techniques are shown to match the expressive power of deep neural network based models in spoken language understanding and speech recognition tasks

Principled methods for mixtures processing

This document is my thesis for getting the habilitation à diriger des recherches, which is the french diploma that is required to fully supervise Ph.D. students. It summarizes the research I did in the last 15 years and also provides the short­term research directions and applications I want to investigate. Regarding my past research, I first describe the work I did on probabilistic audio modeling, including the separation of Gaussian and α­stable stochastic processes. Then, I mention my work on deep learning applied to audio, which rapidly turned into a large effort for community service. Finally, I present my contributions in machine learning, with some works on hardware compressed sensing and probabilistic generative models.My research programme involves a theoretical part that revolves around probabilistic machine learning, and an applied part that concerns the processing of time series arising in both audio and life sciences

Content-based music classification, summarization and retrieval

Ph.DDOCTOR OF PHILOSOPH

Deep learning-based music source separation

Diese Dissertation befasst sich mit dem Problem der Trennung von Musikquellen durch den Einsatz von deep learning Methoden. Die auf deep learning basierende Trennung von Musikquellen wird unter drei Gesichtspunkten untersucht. Diese Perspektiven sind: die Signalverarbeitung, die neuronale Architektur und die Signaldarstellung. Aus der ersten Perspektive, soll verstanden werden, welche deep learning Modelle, die auf DNNs basieren, für die Aufgabe der Musikquellentrennung lernen, und ob es einen analogen Signalverarbeitungsoperator gibt, der die Funktionalität dieser Modelle charakterisiert. Zu diesem Zweck wird ein neuartiger Algorithmus vorgestellt. Der Algorithmus wird als NCA bezeichnet und destilliert ein optimiertes Trennungsmodell, das aus nicht-linearen Operatoren besteht, in einen einzigen linearen Operator, der leicht zu interpretieren ist. Aus der zweiten Perspektive, soll eine neuronale Netzarchitektur vorgeschlagen werden, die das zuvor erwähnte Konzept der Filterberechnung und -optimierung beinhaltet. Zu diesem Zweck wird die als Masker and Denoiser (MaD) bezeichnete neuronale Netzarchitektur vorgestellt. Die vorgeschlagene Architektur realisiert die Filteroperation unter Verwendung skip-filtering connections Verbindungen. Zusätzlich werden einige Inferenzstrategien und Optimierungsziele vorgeschlagen und diskutiert. Die Leistungsfähigkeit von MaD bei der Musikquellentrennung wird durch eine Reihe von Experimenten bewertet, die sowohl objektive als auch subjektive Bewertungsverfahren umfassen. Abschließend, der Schwerpunkt der dritten Perspektive liegt auf dem Einsatz von DNNs zum Erlernen von solchen Signaldarstellungen, für die Trennung von Musikquellen hilfreich sind. Zu diesem Zweck wird eine neue Methode vorgeschlagen. Die vorgeschlagene Methode verwendet ein neuartiges Umparametrisierungsschema und eine Kombination von Optimierungszielen. Die Umparametrisierung basiert sich auf sinusförmigen Funktionen, die interpretierbare DNN-Darstellungen fördern. Der durchgeführten Experimente deuten an, dass die vorgeschlagene Methode beim Erlernen interpretierbarer Darstellungen effizient eingesetzt werden kann, wobei der Filterprozess noch auf separate Musikquellen angewendet werden kann. Die Ergebnisse der durchgeführten Experimente deuten an, dass die vorgeschlagene Methode beim Erlernen interpretierbarer Darstellungen effizient eingesetzt werden kann, wobei der Filterprozess noch auf separate Musikquellen angewendet werden kann. Darüber hinaus der Einsatz von optimal transport (OT) Entfernungen als Optimierungsziele sind für die Berechnung additiver und klar strukturierter Signaldarstellungen.This thesis addresses the problem of music source separation using deep learning methods. The deep learning-based separation of music sources is examined from three angles. These angles are: the signal processing, the neural architecture, and the signal representation. From the first angle, it is aimed to understand what deep learning models, using deep neural networks (DNNs), learn for the task of music source separation, and if there is an analogous signal processing operator that characterizes the functionality of these models. To do so, a novel algorithm is presented. The algorithm, referred to as the neural couplings algorithm (NCA), distills an optimized separation model consisting of non-linear operators into a single linear operator that is easy to interpret. Using the NCA, it is shown that DNNs learn data-driven filters for singing voice separation, that can be assessed using signal processing. Moreover, by enabling DNNs to learn how to predict filters for source separation, DNNs capture the structure of the target source and learn robust filters. From the second angle, it is aimed to propose a neural network architecture that incorporates the aforementioned concept of filter prediction and optimization. For this purpose, the neural network architecture referred to as the Masker-and-Denoiser (MaD) is presented. The proposed architecture realizes the filtering operation using skip-filtering connections. Additionally, a few inference strategies and optimization objectives are proposed and discussed. The performance of MaD in music source separation is assessed by conducting a series of experiments that include both objective and subjective evaluation processes. Experimental results suggest that the MaD architecture, with some of the studied strategies, is applicable to realistic music recordings, and the MaD architecture has been considered one of the state-of-the-art approaches in the Signal Separation and Evaluation Campaign (SiSEC) 2018. Finally, the focus of the third angle is to employ DNNs for learning signal representations that are helpful for separating music sources. To that end, a new method is proposed using a novel re-parameterization scheme and a combination of optimization objectives. The re-parameterization is based on sinusoidal functions that promote interpretable DNN representations. Results from the conducted experimental procedure suggest that the proposed method can be efficiently employed in learning interpretable representations, where the filtering process can still be applied to separate music sources. Furthermore, the usage of optimal transport (OT) distances as optimization objectives is useful for computing additive and distinctly structured signal representations for various types of music sources