A deep matrix factorization method for learning attribute representations

Semi-Non-negative Matrix Factorization is a technique that learns a low-dimensional representation of a dataset that lends itself to a clustering interpretation. It is possible that the mapping between this new representation and our original data matrix contains rather complex hierarchical information with implicit lower-level hidden attributes, that classical one level clustering methodologies can not interpret. In this work we propose a novel model, Deep Semi-NMF, that is able to learn such hidden representations that allow themselves to an interpretation of clustering according to different, unknown attributes of a given dataset. We also present a semi-supervised version of the algorithm, named Deep WSF, that allows the use of (partial) prior information for each of the known attributes of a dataset, that allows the model to be used on datasets with mixed attribute knowledge. Finally, we show that our models are able to learn low-dimensional representations that are better suited for clustering, but also classification, outperforming Semi-Non-negative Matrix Factorization, but also other state-of-the-art methodologies variants.Comment: Submitted to TPAMI (16-Mar-2015

Non-Negative Tensor Factorization Applied to Music Genre Classification

Music genre classification techniques are typically applied to the data matrix whose columns are the feature vectors extracted from music recordings. In this paper, a feature vector is extracted using a texture window of one sec, which enables the representation of any 30 sec long music recording as a time sequence of feature vectors, thus yielding a feature matrix. Consequently, by stacking the feature matrices associated to any dataset recordings, a tensor is created, a fact which necessitates studying music genre classification using tensors. First, a novel algorithm for non-negative tensor factorization (NTF) is derived that extends the non-negative matrix factorization. Several variants of the NTF algorithm emerge by employing different cost functions from the class of Bregman divergences. Second, a novel supervised NTF classifier is proposed, which trains a basis for each class separately and employs basis orthogonalization. A variety of spectral, temporal, perceptual, energy, and pitch descriptors is extracted from 1000 recordings of the GTZAN dataset, which are distributed across 10 genre classes. The NTF classifier performance is compared against that of the multilayer perceptron and the support vector machines by applying a stratified 10-fold cross validation. A genre classification accuracy of 78.9% is reported for the NTF classifier demonstrating the superiority of the aforementioned multilinear classifier over several data matrix-based state-of-the-art classifiers

End-to-end non-negative auto-encoders: a deep neural alternative to non-negative audio modeling

Over the last decade, non-negative matrix factorization (NMF) has emerged as one of the most popular approaches to modeling audio signals. NMF allows us to factorize the magnitude spectrogram to learn representative spectral bases that can be used for a wide range of applications. With the recent advances in deep learning, neural networks (NNs) have surpassed NMF in terms of performance. However, these NNs are trained discriminatively and lack several key characteristics like re-usability and robustness, compared to NMF. In this dissertation, we develop and investigate the idea of end-to-end non-negative autoencoders (NAEs) as an updated deep learning based alternative framework to non-negative audio modeling. We show that end-to-end NAEs combine the modeling advantages of non-negative matrix factorization and the generalizability of neural networks while delivering significant improvements in performance. To this end, we first interpret NMF as a NAE and show that the two approaches are equivalent semantically and in terms of source separation performance. We exploit the availability of sophisticated neural network architectures to propose several extensions to NAEs. We also demonstrate that these modeling improvements significantly boost the performance of NAEs. In audio processing applications, the short-time fourier transform~(STFT) is used as a universal first step and we design algorithms and neural networks to operate on the magnitude spectrograms. We interpret the sequence of steps involved in computing the STFT as additional neural network layers. This enables us to propose end-to-end processing pipelines that operate directly on the raw waveforms. In the context of source separation, we show that end-to-end processing gives a significant improvement in performance compared to existing spectrogram based methods. Furthermore, to train these end-to-end models, we investigate the use of cost functions that are derived from objective evaluation metrics as measured on waveforms. We present subjective listening test results that reveal insights into the performance of these cost functions for end-to-end source separation. Combining the adaptive front-end layers with NAEs, we propose end-to-end NAEs and show how they can be used for end-to-end generative source separation. Our experiments indicate that these models deliver separation performance comparable to that of discriminative NNs, while retaining the modularity of NMF and the modeling flexibility of neural networks. Finally, we present an approach to train these end-to-end NAEs using mixtures only, without access to clean training examples

Nonlinear Hyperspectral Unmixing With Robust Nonnegative Matrix Factorization

International audienceWe introduce a robust mixing model to describe hyperspectral data resulting from the mixture of several pure spectral signatures. The new model extends the commonly used linear mixing model by introducing an additional term accounting for possible nonlinear effects, that are treated as sparsely distributed additive outliers.With the standard nonnegativity and sum-to-one constraints inherent to spectral unmixing, our model leads to a new form of robust nonnegative matrix factorization with a group-sparse outlier term. The factorization is posed as an optimization problem which is addressed with a block-coordinate descent algorithm involving majorization-minimization updates. Simulation results obtained on synthetic and real data show that the proposed strategy competes with state-of-the-art linear and nonlinear unmixing methods

An Efficient Optimal Reconstruction Based Speech Separation Based on Hybrid Deep Learning Technique

Conventional single-channel speech separation has two long-standing issues. The first issue, over-smoothing, is addressed, and estimated signals are used to expand the training data set. Second, DNN generates prior knowledge to address the problem of incomplete separation and mitigate speech distortion. To overcome all current issues, we suggest employing an efficient optimal reconstruction-based speech separation (ERSS) to overcome those problems using a hybrid deep learning technique. First, we propose an integral fox ride optimization (IFRO) algorithm for spectral structure reconstruction with the help of multiple spectrum features: time dynamic information, binaural and mono features. Second, we introduce a hybrid retrieval-based deep neural network (RDNN) to reconstruct the spectrograms size of speech and noise directly. The input signals are sent to Short Term Fourier Transform (STFT). STFT converts a clean input signal into spectrograms then uses a feature extraction technique called IFRO to extract features from spectrograms. After extracting the features, using the RDNN classification algorithm, the classified features are converted to softmax. ISTFT then applies to softmax and correctly separates speech signals. Experiments show that our proposed method achieves the highest gains in SDR, SIR, SAR STIO, and PESQ outcomes of 10.9, 15.3, 10.8, 0.08, and 0.58, respectively. The Joint-DNN-SNMF obtains 9.6, 13.4, 10.4, 0.07, and 0.50, comparable to the Joint-DNN-SNMF. The proposed result is compared to a different method and some previous work. In comparison to previous research, our proposed methodology yields better results

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 audio separation using deep neural networks and matrix factorizations

PhD ThesisSource Separation has become a significant research topic in the signal processing community and the machine learning area. Due to numerous applications, such as automatic speech recognition and speech communication, separation of target speech from the mixed signal is of great importance. In many practical applications, speech separation from a single recorder is most desirable from an application standpoint. In this thesis, two novel approaches have been proposed to address this single channel audio separation problem. This thesis first reviews traditional approaches for single channel source separation, and later elicits a generic approach, which is more capable of feature learning, i.e. deep graphical models. In the first part of this thesis, a novel approach based on matrix factorization and hierarchical model has been proposed. In this work, an artificial stereo mixture is formulated to provide extra information. In addition, a hybrid framework that combines the generalized Expectation-Maximization algorithm with a multiplicative update rule is proposed to optimize the parameters of a matrix factorization based approach to approximatively separate the mixture. Furthermore, a hierarchical model based on an extreme learning machine is developed to check the validity of the approximately separated sources followed by an energy minimization method to further improve the quality of the separated sources by generating a time-frequency mask. Various experiments have been conducted and the obtained results have shown that the proposed approach outperforms conventional approaches not only in reduction of computational complexity, but also the separation performance. In the second part, a deep neural network based ensemble system is proposed. In this work, the complementary property of different features are fully explored by ‘wide’ and ‘forward’ ensemble system. In addition, instead of using the features learned from the output layer, the features learned from the penultimate layer are investigated. The final embedded features are classified with an extreme learning machine to generate a binary mask to separate a mixed signal. The experiment focuses on speech in the presence of music and the obtained results demonstrated that the proposed ensemble system has the ability to explore the complementary property of various features thoroughly under various conditions with promising separation performance