Unsupervised Learning for Monaural Source Separation Using Maximization–Minimization Algorithm with Time–Frequency Deconvolution

This paper presents an unsupervised learning algorithm for sparse nonnegative matrix factor time–frequency deconvolution with optimized fractional β -divergence. The β -divergence is a group of cost functions parametrized by a single parameter β . The Itakura–Saito divergence, Kullback–Leibler divergence and Least Square distance are special cases that correspond to β=0, 1, 2 , respectively. This paper presents a generalized algorithm that uses a flexible range of β that includes fractional values. It describes a maximization–minimization (MM) algorithm leading to the development of a fast convergence multiplicative update algorithm with guaranteed convergence. The proposed model operates in the time–frequency domain and decomposes an information-bearing matrix into two-dimensional deconvolution of factor matrices that represent the spectral dictionary and temporal codes. The deconvolution process has been optimized to yield sparse temporal codes through maximizing the likelihood of the observations. The paper also presents a method to estimate the fractional β value. The method is demonstrated on separating audio mixtures recorded from a single channel. The paper shows that the extraction of the spectral dictionary and temporal codes is significantly more efficient by using the proposed algorithm and subsequently leads to better source separation performance. Experimental tests and comparisons with other factorization methods have been conducted to verify its efficacy

Statistical single channel source separation

PhD ThesisSingle channel source separation (SCSS) principally is one of the challenging fields in signal processing and has various significant applications. Unlike conventional SCSS methods which were based on linear instantaneous model, this research sets out to investigate the separation of single channel in two types of mixture which is nonlinear instantaneous mixture and linear convolutive mixture. For the nonlinear SCSS in instantaneous mixture, this research proposes a novel solution based on a two-stage process that consists of a Gaussianization transform which efficiently compensates for the nonlinear distortion follow by a maximum likelihood estimator to perform source separation. For linear SCSS in convolutive mixture, this research proposes new methods based on nonnegative matrix factorization which decomposes a mixture into two-dimensional convolution factor matrices that represent the spectral basis and temporal code. The proposed factorization considers the convolutive mixing in the decomposition by introducing frequency constrained parameters in the model. The method aims to separate the mixture into its constituent spectral-temporal source components while alleviating the effect of convolutive mixing. In addition, family of Itakura-Saito divergence has been developed as a cost function which brings the beneficial property of scale-invariant. Two new statistical techniques are proposed, namely, Expectation-Maximisation (EM) based algorithm framework which maximizes the log-likelihood of a mixed signals, and the maximum a posteriori approach which maximises the joint probability of a mixed signal using multiplicative update rules. To further improve this research work, a novel method that incorporates adaptive sparseness into the solution has been proposed to resolve the ambiguity and hence, improve the algorithm performance. The theoretical foundation of the proposed solutions has been rigorously developed and discussed in details. Results have concretely shown the effectiveness of all the proposed algorithms presented in this thesis in separating the mixed signals in single channel and have outperformed others available methods.Universiti Teknikal Malaysia Melaka(UTeM), Ministry of Higher Education of Malaysi

An investigation of the utility of monaural sound source separation via nonnegative matrix factorization applied to acoustic echo and reverberation mitigation for hands-free telephony

In this thesis we investigate the applicability and utility of Monaural Sound Source Separation (MSSS) via Nonnegative Matrix Factorization (NMF) for various problems related to audio for hands-free telephony. We first investigate MSSS via NMF as an alternative acoustic echo reduction approach to existing approaches such as Acoustic Echo Cancellation (AEC). To this end, we present the single-channel acoustic echo problem as an MSSS problem, in which the objective is to extract the users signal from a mixture also containing acoustic echo and noise. To perform separation, NMF is used to decompose the near-end microphone signal onto the union of two nonnegative bases in the magnitude Short Time Fourier Transform domain. One of these bases is for the spectral energy of the acoustic echo signal, and is formed from the in- coming far-end user’s speech, while the other basis is for the spectral energy of the near-end speaker, and is trained with speech data a priori. In comparison to AEC, the speaker extraction approach obviates Double-Talk Detection (DTD), and is demonstrated to attain its maximal echo mitigation performance immediately upon initiation and to maintain that performance during and after room changes for similar computational requirements. Speaker extraction is also shown to introduce distortion of the near-end speech signal during double-talk, which is quantified by means of a speech distortion measure and compared to that of AEC. Subsequently, we address Double-Talk Detection (DTD) for block-based AEC algorithms. We propose a novel block-based DTD algorithm that uses the available signals and the estimate of the echo signal that is produced by NMF-based speaker extraction to compute a suitably normalized correlation-based decision variable, which is compared to a fixed threshold to decide on doubletalk. Using a standard evaluation technique, the proposed algorithm is shown to have comparable detection performance to an existing conventional block-based DTD algorithm. It is also demonstrated to inherit the room change insensitivity of speaker extraction, with the proposed DTD algorithm generating minimal false doubletalk indications upon initiation and in response to room changes in comparison to the existing conventional DTD. We also show that this property allows its paired AEC to converge at a rate close to the optimum. Another focus of this thesis is the problem of inverting a single measurement of a non- minimum phase Room Impulse Response (RIR). We describe the process by which percep- tually detrimental all-pass phase distortion arises in reverberant speech filtered by the inverse of the minimum phase component of the RIR; in short, such distortion arises from inverting the magnitude response of the high-Q maximum phase zeros of the RIR. We then propose two novel partial inversion schemes that precisely mitigate this distortion. One of these schemes employs NMF-based MSSS to separate the all-pass phase distortion from the target speech in the magnitude STFT domain, while the other approach modifies the inverse minimum phase filter such that the magnitude response of the maximum phase zeros of the RIR is not fully compensated. Subjective listening tests reveal that the proposed schemes generally produce better quality output speech than a comparable inversion technique

An investigation of the utility of monaural sound source separation via nonnegative matrix factorization applied to acoustic echo and reverberation mitigation for hands-free telephony

In this thesis we investigate the applicability and utility of Monaural Sound Source Separation (MSSS) via Nonnegative Matrix Factorization (NMF) for various problems related to audio for hands-free telephony. We first investigate MSSS via NMF as an alternative acoustic echo reduction approach to existing approaches such as Acoustic Echo Cancellation (AEC). To this end, we present the single-channel acoustic echo problem as an MSSS problem, in which the objective is to extract the users signal from a mixture also containing acoustic echo and noise. To perform separation, NMF is used to decompose the near-end microphone signal onto the union of two nonnegative bases in the magnitude Short Time Fourier Transform domain. One of these bases is for the spectral energy of the acoustic echo signal, and is formed from the in- coming far-end user’s speech, while the other basis is for the spectral energy of the near-end speaker, and is trained with speech data a priori. In comparison to AEC, the speaker extraction approach obviates Double-Talk Detection (DTD), and is demonstrated to attain its maximal echo mitigation performance immediately upon initiation and to maintain that performance during and after room changes for similar computational requirements. Speaker extraction is also shown to introduce distortion of the near-end speech signal during double-talk, which is quantified by means of a speech distortion measure and compared to that of AEC. Subsequently, we address Double-Talk Detection (DTD) for block-based AEC algorithms. We propose a novel block-based DTD algorithm that uses the available signals and the estimate of the echo signal that is produced by NMF-based speaker extraction to compute a suitably normalized correlation-based decision variable, which is compared to a fixed threshold to decide on doubletalk. Using a standard evaluation technique, the proposed algorithm is shown to have comparable detection performance to an existing conventional block-based DTD algorithm. It is also demonstrated to inherit the room change insensitivity of speaker extraction, with the proposed DTD algorithm generating minimal false doubletalk indications upon initiation and in response to room changes in comparison to the existing conventional DTD. We also show that this property allows its paired AEC to converge at a rate close to the optimum. Another focus of this thesis is the problem of inverting a single measurement of a non- minimum phase Room Impulse Response (RIR). We describe the process by which percep- tually detrimental all-pass phase distortion arises in reverberant speech filtered by the inverse of the minimum phase component of the RIR; in short, such distortion arises from inverting the magnitude response of the high-Q maximum phase zeros of the RIR. We then propose two novel partial inversion schemes that precisely mitigate this distortion. One of these schemes employs NMF-based MSSS to separate the all-pass phase distortion from the target speech in the magnitude STFT domain, while the other approach modifies the inverse minimum phase filter such that the magnitude response of the maximum phase zeros of the RIR is not fully compensated. Subjective listening tests reveal that the proposed schemes generally produce better quality output speech than a comparable inversion technique

Single channel blind source separation

Single channel blind source separation (SCBSS) is an intensively researched field with numerous important applications. This research sets out to investigate the separation of monaural mixed audio recordings without relying on training knowledge. This research proposes a novel method based on variable regularised sparse nonnegative matrix factorization which decomposes an information-bearing matrix into two-dimensional convolution of factor matrices that represent the spectral basis and temporal code of the sources. In this work, a variational Bayesian approach has been developed for computing the sparsity parameters of the matrix factorization. To further improve the previous work, this research proposes a new method based on decomposing the mixture into a series of oscillatory components termed as the intrinsic mode functions (IMF). It is shown that IMFs have several desirable properties unique to SCBSS problem and how these properties can be advantaged to relax the constraints posed by the problem. In addition, this research develops a novel method for feature extraction using psycho-acoustic model. The monaural mixed signal is transformed to a cochleagram using the gammatone filterbank, whose bandwidths increase incrementally as the center frequency increases; thus resulting to non-uniform time-frequency (TF) resolution in the analysis of audio signal. Within this domain, a family of Itakura-Saito (IS) divergence based novel two-dimensional matrix factorization has been developed. The proposed matrix factorizations have the property of scale invariant which enables lower energy components in the cochleagram to be treated with equal importance as the high energy ones. Results show that all the developed algorithms presented in this thesis have outperformed conventional methods.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

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

잡음에 강인한 음성 구간 검출과 음성 향상을 위한 딥 러닝 기반 기법 연구

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2017. 2. 김남수.Over the past decades, a number of approaches have been proposed to improve the performances of voice activity detection (VAD) and speech enhancement algorithms which are crucial for speech communication and speech signal processing systems. In particular, the increasing use of machine learning-based techniques has led to the more robust algorithms in low SNR conditions. Among them, the deep neural network (DNN) has been one of the most popular techniques. While the DNN-based technique is successfully applied to these tasks, the characteristics of VAD and speech enhancement tasks are not fully incorporated to the DNN structures and objective functions. In this thesis, we propose the novel training schemes and post-filter for DNN-based VAD and speech enhancement. Unlike algorithms with basic DNN-based framework, the proposed algorithm combines the knowledge from signal processing and machine learning society to develop the improve DNN-based VAD and speech enhancement algorithm. In the following chapters, the environmental mismatch problem in the VAD area is compensated by applying multi-task learning to the DNN-based VAD. Also, the DNN-based framework is proposed in the speech enhancement scenario and the novel objective function and post-filter which are derived from the characteristics on human auditory perception improve the DNN-based speech enhancement algorithm. In the VAD task, the DNN-based algorithm was recently proposed and outperformed the traditional and other machine learning-based VAD algorithms. However, the performance of the DNN-based algorithm sometimes deteriorates when the training and test environments are not matched with each other. In order to increase the performance of the DNN-based VAD in unseen environments, we adopt the multi-task learning (MTL) framework which consists of the primary VAD and subsidiary feature enhancement tasks. By employing the MTL framework, the DNN learns the denoising function in the shared hidden layers that is useful to maintain the VAD performance in mismatched noise conditions. Second, the DNN-based framework is applied to the speech enhancement by considering it as a regression task. The encoding vector of the conventional nonnegative matrix factorization (NMF)-based algorithm is estimated by the proposed DNN and the performance of the DNN-based algorithm is compared to the conventional NMF-based algorithm. Third, the perceptually motivated objective function is proposed for the DNN-based speech enhancement. In the proposed technique, a new objective function which consists of the Mel-scale weighted mean square error, temporal and spectral variations similarities between the enhanced and clean speech is employed in the DNN training stage. The proposed objective function helps to compute the gradients based on a perceptually motivated non-linear frequency scale and alleviates the over-smoothness of the estimated speech. Furthermore, the post-filter which adjusts the variance over frequency bins further compensates the lack of contrasts between spectral peaks and valleys in the enhanced speech. The conventional GV equalization post-filters do not consider the spectral dynamics over frequency bins. To consider the contrast between spectral peaks and valleys in each enhanced speech frames, the proposed algorithm matches the variance over coefficients in the log-power spectra domain. Finally, in the speech enhancement task, an integrated technique using the proposed perceptually motivated objective function and the post-filter is described. In matched and mismatched noise conditions, the performance results of the conventional and proposed algorithm are discussed. Also, the subjective preference test result of these algorithms is also provided.1 Introduction 1 2 Conventional Approaches for Speech Enhancement 7 2.1 NMF-Based Speech Enhancement 7 3 Deep Neural Networks 13 3.1 Introduction 13 3.2 Objective Function 14 3.3 Stochastic Gradient Descent 16 4 DNN-Based Voiced Activity Detection with Multi-Task Learning Framework 19 4.1 Introduction 19 4.2 DNN-Based VAD Algorithm 21 4.3 DNN-Based VAD with MTL framework 23 4.4 Experimental Results 26 4.4.1 Experiments in Matched Noise Conditions 26 4.4.2 Experiments in Mismatched Noise Conditions 28 4.5 Summary 30 5 NMF-based Speech Enhancement Using Deep Neural Network 35 5.1 Introduction 35 5.2 Encoding Vector Estimation Using DNN 37 5.3 Experiments 42 5.4 Summary 47 6 DNN-Based Monaural Speech Enhancement with Temporal and Spectral Variations Equalization 49 6.1 Introduction 49 6.2 Conventional DNN-Based Speech Enhancement 53 6.2.1 Training Stage 53 6.2.2 Test Stage 55 6.3 Perceptually-Motivated Criteria 56 6.3.1 Perceptually Motivated Objective Function 56 6.3.2 Mel-Scale Weighted Mean Square Error 58 6.3.3 Temporal Variation Similarity 58 6.3.4 Spectral Variation Similarity 61 6.3.5 DNN Training with the Proposed Objective Function 62 6.4 Experiments 62 6.4.1 Performance Evaluation with Varying Weight Parameters 64 6.4.2 Performance Evaluation in Matched Noise Conditions 64 6.4.3 Performance Evaluation in Mismatched Noise Conditions 66 6.4.4 Comparison Between Variation Analysis Method 66 6.4.5 Subjective Test Results 67 6.5 Summary 68 7 Spectral Variance Equalization Post-filter for DNN-Based Speech Enhancement 75 7.1 Introduction 75 7.2 GV Equalization Post-Filter 76 7.3 Spectral Variance(SV) Equalization Post-Filter 77 7.4 Experiments 78 7.4.1 Objective Test Results 78 7.4.2 Subjective Test Results 79 7.5 Summary 81 8 Conclusions 83 Bibliography 85 Appendix 95 요약 97Docto