30 research outputs found

    고유 특성을 활용한 음악에서의 보컬 분리

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    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 Dynamics - Towards a Perceptual Model of Punch

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    This thesis discusses research conducted towards the development of an objective model that predicts punch in musical signals. Punch is a term often used by engineers and producers when describing a particular perceptual sensation found in produced music. Music is often characterised by listeners as being punchier yet the term is subjective, in terms of its meaning and the subsequent auditory effect on the listener. An objective model of punch would therefore prove useful for both music classification purposes and as a possible further metric that could be employed in music production and mastering metering tools. The literature reviewed within this body of work encompasses both subjective and objective audio evaluation methods in addition to low-level signal extraction and measurement techniques. The review concludes that whilst there has been a great deal of work in the area of semantic description and audio quality measurement, low-level analysis with respect to the perception of punch remains largely unexplored. The project was completed in a number of phases each designed to investigate the perceptual effects resulting from manipulation of test stimuli. The rationale behind this testing was to establish the key low-level descriptors relating to the punch attribute with the aim of producing a final objective and perceptually based model. The listening tests in each phase were conducted according to the ITU-R BS 1534-1 recommendation. In producing an objective model for the prediction of punch, listener perception to the attribute shows a strong correlation to the signal onset times, octave frequency band, signal duration and dynamic range. The punch measure obtained using the model is named PM95, where 95 indicates the upper percentile used in the measurement. Secondary measures were also obtained as a result of the iterative approach adopted. These are Inter-Band-Ratio (IBR), Transient to Steady-state Ratio (TSR) and Transient to Steady-state Ratio+Residual (TSR+R). These measures are useful in quantifying overall audio quality with respect to its dynamic range across frequency bands in addition to being a more reliable metric for defining the overall compression being applied to a piece of music. In addition, the latter two measures proposed may be useful in highlighting perceptual masking artefacts. The completed perceptual punch model was validated using the scores obtained from a large scale and independently conducted forced pairwise comparison test using expert listeners and a wide range of musical stimuli. From the results obtained, the PM95 measure showed a ‘very strong’ positive correlation with listener punch perception. Both r and rho coefficients (0.849 and 0.833) being significant at the 0.01 level (2-tailed). The PM95M measure, which is the PM95 measure divided by the mean value of punch frames also correlated very well with the perceptual punch scale having both r and rho coefficients (0.707 and -0.750) being significant at the 0.05 level (2-tailed). A real-time implementation of the punch model (and other measures proposed in this thesis) could be utilised as extensions to the metrics currently being used in Music Information Retrieval

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

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    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

    An review of automatic drum transcription

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    In Western popular music, drums and percussion are an important means to emphasize and shape the rhythm, often defining the musical style. If computers were able to analyze the drum part in recorded music, it would enable a variety of rhythm-related music processing tasks. Especially the detection and classification of drum sound events by computational methods is considered to be an important and challenging research problem in the broader field of Music Information Retrieval. Over the last two decades, several authors have attempted to tackle this problem under the umbrella term Automatic Drum Transcription(ADT).This paper presents a comprehensive review of ADT research, including a thorough discussion of the task-specific challenges, categorization of existing techniques, and evaluation of several state-of-the-art systems. To provide more insights on the practice of ADT systems, we focus on two families of ADT techniques, namely methods based on Nonnegative Matrix Factorization and Recurrent Neural Networks. We explain the methods’ technical details and drum-specific variations and evaluate these approaches on publicly available datasets with a consistent experimental setup. Finally, the open issues and under-explored areas in ADT research are identified and discussed, providing future directions in this fiel

    A review of differentiable digital signal processing for music and speech synthesis

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    The term “differentiable digital signal processing” describes a family of techniques in which loss function gradients are backpropagated through digital signal processors, facilitating their integration into neural networks. This article surveys the literature on differentiable audio signal processing, focusing on its use in music and speech synthesis. We catalogue applications to tasks including music performance rendering, sound matching, and voice transformation, discussing the motivations for and implications of the use of this methodology. This is accompanied by an overview of digital signal processing operations that have been implemented differentiably, which is further supported by a web book containing practical advice on differentiable synthesiser programming (https://intro2ddsp.github.io/). Finally, we highlight open challenges, including optimisation pathologies, robustness to real-world conditions, and design trade-offs, and discuss directions for future research

    Guided Matching Pursuit and its Application to Sound Source Separation

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    In the last couple of decades there has been an increasing interest in the application of source separation technologies to musical signal processing. Given a signal that consists of a mixture of musical sources, source separation aims at extracting and/or isolating the signals that correspond to the original sources. A system capable of high quality source separation could be an invaluable tool for the sound engineer as well as the end user. Applications of source separation include, but are not limited to, remixing, up-mixing, spatial re-configuration, individual source modification such as filtering, pitch detection/correction and time stretching, music transcription, voice recognition and source-specific audio coding to name a few. Of particular interest is the problem of separating sources from a mixture comprising two channels (2.0 format) since this is still the most commonly used format in the music industry and most domestic listening environments. When the number of sources is greater than the number of mixtures (which is usually the case with stereophonic recordings) then the problem of source separation becomes under-determined and traditional source separation techniques, such as “Independent Component Analysis” (ICA) cannot be successfully applied. In such cases a family of techniques known as “Sparse Component Analysis” (SCA) are better suited. In short a mixture signal is decomposed into a new domain were the individual sources are sparsely represented which implies that their corresponding coefficients will have disjoint (or almost) disjoint supports. Taking advantage of this property along with the spatial information within the mixture and other prior information that could be available, it is possible to identify the sources in the new domain and separate them by going back to the time domain. It is a fact that sparse representations lead to higher quality separation. Regardless, the most commonly used front-end for a SCA system is the ubiquitous short-time Fourier transform (STFT) which although is a sparsifying transform it is not the best choice for this job. A better alternative is the matching pursuit (MP) decomposition. MP is an iterative algorithm that decomposes a signal into a set of elementary waveforms called atoms chosen from an over-complete dictionary in such a way so that they represent the inherent signal structures. A crucial part of MP is the creation of the dictionary which directly affects the results of the decomposition and subsequently the quality of source separation. Selecting an appropriate dictionary could prove a difficult task and an adaptive approach would be appropriate. This work proposes a new MP variant termed guided matching pursuit (GMP) which adds a new pre-processing step into the main sequence of the MP algorithm. The purpose of this step is to perform an analysis of the signal and extract important features, termed guide maps, that are used to create dynamic mini-dictionaries comprising atoms which are expected to correlate well with the underlying signal structures thus leading to focused and more efficient searches around particular supports of the signal. This algorithm is accompanied by a modular and highly flexible MATLAB implementation which is suited to the processing of long duration audio signals. Finally the new algorithm is applied to the source separation of two-channel linear instantaneous mixtures and preliminary testing demonstrates that the performance of GMP is on par with the performance of state of the art systems

    Principled methods for mixtures processing

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    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

    Proceedings of the EAA Spatial Audio Signal Processing symposium: SASP 2019

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    International audienc

    A matching filter and envelope system for timbral blending of the bass guitar

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    A method of intelligent filter curve estimation from signal spectra is investigated to assess its viability of blending the perceived timbres of two signals together. By influencing the spectrum of a source signal with that of a modifier, its magnitude spectrum will be reshaped to resemble the modifier signal and reflect some of its timbral characteristics more closely. A system of transplanting the time-domain signal envelope of a signal onto a host is also presented in a combined system. The intended purpose of such a system is in the development of a hybrid acoustic-electric instrument where the timbral products of an expressive performance may be used to manipulate the spectrum and envelope of musical signals. A bass guitar is studied as the source instrument given the wide range of expressive techniques that may be executed on the instrument. Further analysis of spectra gathered from bass guitar performance techniques are used to provide deeper insight into performance techniques that may be performed on the instrument
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