Degenerate Unmixing Estimation Technique Research Articles

Joint short-time speaker recognition and tracking using sparsity-based source detection

A random finite set-based sequential Monte–Carlo tracking method is proposed to track multiple acoustic sources in indoor scenarios. The proposed method can improve tracking performance by introducing recognized speaker identities from the received signals. At the front-end, the degenerate unmixing estimation technique (DUET) is employed to separate the mixed signals, and the time delay of arrival (TDOA) is measured. In addition, a criterion to select the reliable microphone pair is designed to quickly obtain accurate speaker identities from the mixed signals, and the Gaussian mixture model universal background model (GMM-UBM) is employed to train the speaker model. In the tracking step, the update of the weight for each particle is derived after introducing the recognized speaker identities, which results in better association between the measurements and sources. Simulation results demonstrate that the proposed method can improve the accuracy of the filter states and discriminate the sources close to each other.

Improving time–frequency sparsity for enhanced audio source separation in degenerate unmixing estimation technique algorithm

In recent years, much research has been focused on separating acoustic sources from their mixtures. Degenerate Unmixing Estimation Technique (DUET) is one of the widely popular methods of Blind Source Separation (BSS) in underdetermined scenarios. DUET is based on a signal recovery sparsity algorithm whose performance is strongly influenced by sparsity in the Time-Frequency (TF) domain. Noises and an several sources in mixtures limit the sparsity resulting in performance degradation in DUET. Here an enhanced strategy has been adopted by combining DUET with adaptive noise cancellation utilising the Dual-Tree Complex Wavelet Transform (DTCWT) as a pre-processor and TF refinement utilising Synchroextracting Transform (SET) as a post-processor. This improves the sparsity of sources and energy concentrations in a TF representation. Results of the signal separation performance evaluation reveal that the proposed algorithm outperforms conventional DUET in signal separation, especially in real-time scenarios.

Performance of Digital Drone Signage System Based on DUET

In this letter, we study a scenario based on degenerate unmixing estimation technique (DUET) that separates original signals from mixture of FHSS signals with two antennas. We have shown that the assumptions for separating mixed signals in DUET can be applied to drone based digital signage recognition signals and proposed the DUET-based separation scheme (DBSS) to classify the mixed recognition drone signals by extracting the delay and attenuation components of the mixture signal through the likelihood function and the short-term Fourier transform (STFT). In addition, we propose an iterative algorithm for signal separation with the conventional DUET scheme. Numerical results showed that the proposed algorithm is more separation-efficient compared to baseline schemes. DBSS can separate all signals within about 0.56 seconds when there are fewer than nine signage signals.

Blind disturbance separation and identification in a transitional boundary layer using minimal sensing

A novel approach is presented for identifying disturbance sources in wall-bounded shear flows. The underlying approach models the flow state, as measured by sensors embedded in the flow, as a mixture of disturbance sources. The degenerate unmixing estimation technique is adopted as a blind source separation technique to recover the separate sources and their unknown mixing process. The efficiency of this approach stems from its ability to isolate any, a priori unknown, number of sources, using two sensors only. Furthermore, by adding a single additional sensor, the method is expanded to also determine the propagation velocity vector of each of the isolated sources, based on sensor readings from three sensors appropriately located in the flow field. Theoretical guidelines for locating the sensors are provided. The power of the method is demonstrated via computer simulations and wind-tunnel experiments. The numerical study considers disturbances comprising discrete Tollmien–Schlichting waves and wave packets. Linear stability theory is used to model source mixtures acquired by sensors placed in a Blasius boundary layer. The experimental study investigates the flow over a flat plate, with hot wires as sensors, and a loudspeaker and plasma actuators as source generators. Based on numerical and experimental demonstrations, it is believed that the new approach should prove useful in various applications, including active control of boundary layer transition from laminar to turbulent flow.

High clarity speech separation using synchro extracting transform

Degenerate unmixing estimation technique (DUET) is the most ideal blind source separation (BSS) method for underdetermined conditions with number of sources exceeds number of mixtures. Estimation of mixing parameters which is the most critical step in the DUET algorithm, is developed based on the characteristic feature of sparseness of speech signals in time frequency (TF) domain. Hence, DUET relies on the clarity of time frequency representation (TFR) and even the slightest interference in the TF plane will be detrimental to the unmixing performance. In conventional DUET algorithm, short time Fourier transform (STFT) is utilized for extracting the TFR of speech signals. However, STFT can provide on limited sharpness to the TFR due to its inherent conceptual limitations, which worsens under noise contamination. This paper presents the application of post-processing techniques like synchro squeezed transform (SST) and synchro extracting transform (SET) to the DUET algorithm, to improve the TF resolution. The performance enhancement is evaluated both qualitatively and quantitatively by visual inspection, Renyi entropy of TFR and objective measures of speech signals. The results show enhancement in TF resolution and high clarity signal reconstruction. The method also provides adequate robustness to noise contamination.

Audio source separation to reduce sleeping partner sounds: a simulation study

Objective. When recording a subject in an at-home environment for sleep evaluation or for other breathing disorder diagnoses using non-contact microphones, the breathing recordings (audio signals) can be distorted by sounds such as TV, outside noise, or air-conditioners. If two people are sleeping together, both may produce breathing/snoring sounds that need to be separated. In this study, we present signal processing and source separation algorithms for the enhancement of individual breathing/snoring audio signals in a simulated environment. Approach. We developed a computer simulation of mixed signals derived from genuine nocturnal recordings of 110 subjects. Two main source separation approaches were tested: (1) changing the basis vectors for the mixtures in the time domain (principal and independent component analysis, PCA/ICA) and (2) converting the mixtures to their time-frequency representations (degenerate un-mixing estimation technique, DUET). In addition to these source separation techniques, a beamforming approach was tested. Main results. The separation results with a reverberation time of 0.15 s and zero SNR between signals showed good performance (mean source to interference ratio (SIR): DUET = 12.831 dB, ICA = 3.388 dB, PCA = 4.452 dB), and for beamforming (SIR = −0.304 dB). To evaluate our source separation results, we propose two new measures: an evaluation measure based on a spectral similarity score (mel-SID) between the target source and its estimation (after separation) and a breathing energy ratio measure (BER). The results with the new proposed measures yielded comparable conclusions (mel-SID: DUET = 1.320, ICA = 2.732, PCA = 1.927, and beamforming = 2.590, BER: DUET = 10.241 dB, ICA = 0.270 dB, PCA = −2.847 dB, and beamforming = −1.151 dB), but better differentiated the differences between the performance of the algorithms. The DUET is superior on all measures. Its main advantage is that it only uses two microphones for separation. Significance. The separated audio signal can thus contribute to a more informed diagnosis of sleep-related and non-sleep-related diseases. The Institutional Review Committee of Soroka University Medical Center approved this study protocol (protocol number 10141) and all methods were performed in accordance with the relevant guidelines and regulations.

Unique Methods for Determining the Attenuation and Delay in Blind Source Separation Based on the Degenerate Unmixing Estimation Technique

The degenerate unmixing estimation technique (DUET), enables facile sound source separation through two signal mixtures. However, in numerous practical situations, identifying peaks in DUET histograms is difficult, which in turn prevents effective source separation. Specifically, sound sources with short time percentages in mixtures are more unlikely to form peaks in a histogram. Besides, when the noise floor of mixtures is high, the height of the histogram peak may be reduced, which causes peak position bias and prevents effective source separation. In this study, unique methods are proposed to solve the aforementioned problems. A subhistogram is established for each time frame; thus, the original histogram is divided into a series of subhistograms. Two measurement indices are then proposed to identify the accurate attenuation and delay parameters for sound sources: maximum distribution, which is based on the infinity norm, and variance distribution, which is based on variances. The results verify that maximum distribution effectively highlights the peaks of instantaneous sound sources when the time percentage difference between sound sources is excessively high. In this scenario, the conventional histogram is unable to find the peaks. When a strong noise disturbance is present in mixtures, variance distribution can be employed to estimate peak positions with lower biases than those in the conventional histogram. In addition to more well-defined and intense peaks, the proposed method can also reduce the bias error by 71% when the SNR is 0 dB. Variance distribution is more robust against noise disturbance compared to conventional histograms and can be adopted in a wide range of applications.

Clustering of spatial cues by semantic segmentation for anechoic binaural source separation

The recent introduction of neural networks to speech separation has dramatically boosted the separation performance. This paper presents a novel psychoacoustic approach for speech source separation in anechoic conditions, using semantic segmentation of the interaural spectrograms of the audio mixtures. We have trained two separate U-Nets (a specialized neural network for semantic segmentation) on the interaural level difference (ILD) spectrogram, and the interaural phase difference (IPD) spectrogram of a single source. After training, these U-Nets are used to predict the class of each time frequency (TF) unit of the interaural spectrogram of the audio mixture. The ILD and IPD soft masks obtained from these U-Nets are combined by a novel scheme which utilizes the strength of the interaural cues in different frequency bands. The results show improved separation over two state of the art machine learning source separation systems utilizing the same interaural cues. There is average improvement of 7.32 dB in signal to distortion ratio (SDR) and 0.3 points improvement in short term objective intelligibility (STOI) over degenerate un-mixing estimation technique (DUET) algorithm and 2.51 dB improvement in SDR with comparable intelligibility over model-based expectation–maximization source separation and localization (MESSL) algorithm.

Degenerate unmixing estimation technique of speech mixtures in real environments using wavelets

ABSTRACT Humans are exceptionally good at focusing their attention on a particular person in a noisy environment, mentally muting all other voices and sounds known as cocktail party effect, and this capability comes naturally to them. Although human brain and auditory system can handle this problem with ease, it becomes very hard to solve with computational algorithms. A novel technique is proposed in this paper for separating speech signals, when the number of sources is more than the sensors. Here wavelets are used in initial pre-processing and time-frequency analysis of mixtures to extract the mixing variables which makes it useful in applications involving real-time scenario. The algorithm also converges at a faster rate as we use automatic peak tracking algorithm to track the peak and constructing binary masks. The simulation results on mixtures of speech signals not only show improvement in separation in reverberant conditions but also show better separation results and improved perceptual quality of separated sources in real-world noisy environments.

Degenerate Unmixing Estimation Technique (DUET) for Fetal ECG Blind Source Separation

Monitoring the health of fetus at early stage is very crucial. One of the non-invasive ways is by evaluating the pattern of electrocardiogram (ECG) signals of mother’s abdomen and thorax. As these raw signals are mixed signals that consist of mainly maternal ECG (MECG) and fetal ECG (FECG), an effective extraction method of FECG from the mixed signals is imperative. Fast Independent Component Analysis (FastICA) is one of the common signal processing algorithms for blind source separation (BSS). However, it works only for even-determined case when the number of sources (MECG and FECG) is equal to the number of mixtures (two mixed signals i.e. from the mother’s abdomen and the mother’s thorax). For the underdetermined case in which the number of sources would be more than the number of mixture (for the case of twins or triplets’ pregnancy), FastICA algorithm fails as the computation of the inverse mixing matrix of ICA is theoritically impossible. Thus, Degenerate Unmixing Estimation Technique (DUET) algorithm which is based on signal recovery sparsity algorithm is implemented in this research so as to discover its feasibility to solve both even-determined and underdetermined cases. From the experimental results, the DUET performance is promising. Although for even-determined case, FastICA is better than DUET performance, DUET is proved to be valuable in solving the underdetermined case problem, yet the low FECG signal to noise ratio (SNR) value is observed reflecting the high interference.

Aliasing Signal Separation of Superimposed Abrasive Debris Based on Degenerate Unmixing Estimation Technique.

Leakage is the most important failure mode in aircraft hydraulic systems caused by wear and tear between friction pairs of components. The accurate detection of abrasive debris can reveal the wear condition and predict a system’s lifespan. The radial magnetic field (RMF)-based debris detection method provides an online solution for monitoring the wear condition intuitively, which potentially enables a more accurate diagnosis and prognosis on the aviation hydraulic system’s ongoing failures. To address the serious mixing of pipe abrasive debris, this paper focuses on the superimposed abrasive debris separation of an RMF abrasive sensor based on the degenerate unmixing estimation technique. Through accurately separating and calculating the morphology and amount of the abrasive debris, the RMF-based abrasive sensor can provide the system with wear trend and sizes estimation of the wear particles. A well-designed experiment was conducted and the result shows that the proposed method can effectively separate the mixed debris and give an accurate count of the debris based on RMF abrasive sensor detection.

Separation of multiple speech sources by recovering sparse and non-sparse components from B-format microphone recordings

This paper proposes a blind source separation (BSS) method for recovering multiple speech sources from sound fields recorded by a B-format microphone. This microphone provides a four channel representation that can be used to derive the direction of arrival (DOA) of spatially distinct time-frequency (TF) components. Such sparse components correspond to bins where only one speech source is active and are identified based on the inter-correlation among the mixture signals. They are recovered via a degenerate unmixing estimation technique (DUET)-like method. Proposed is a “local-zone stationarity” assumption, where the amplitude of a speech signal remains approximately constant within a small band of TF components. This assumption is validated through statistical analysis of a quantitative measure of stationarity. Under this assumption, the non-sparse components (TF points where more than one speech source is active) are recovered via a Wiener-filter-like approach where the separated sparse components is utilized as a guide. The final separated sources are obtained by combining the separated sparse and non-sparse components. Both objective and subjective evaluations show that the proposed method achieves better separation quality compared to some existing BSS approaches where up to six simultaneous speech sources are considered.

Implementation of Blind Speech Separation for Intelligent Humanoid Robot using DUET Method

Nowadays, there are many efforts in building intelligent humanoid robot and adding advanced ability such as Blind Speech Separation (BSS). BSS is a problem of separation of several speech signals in a real world from mono or stereo audio record. In this research, we implement BSP system using DUET algorithm which allow to separate any number of sources by using only stereo (two) mixtures. The DUET (Degenerate Unmixing Estimation Technique) algorithm replaces our previous FastICA (Fast Independent Component Analysis) method only success in simulation but failed in the implementation. The main problem of FastICA is that it assumes instantaneous mixing without time delay in the recording process. To deals with audio record in the presence of inevitable time delays, it has to be replaced with DUET algorithm to separate well in real time. Finally, the DUET algorithm is implemented to humanoid robot which is developed using Raspberry Pi and equipped with RaspPi Cam to detect human face. Furthermore, the Cirrus Logic Audio Card is stacked to Raspberry Pi in order to record stereo audio. In our experiments, there are three controlled variables to evaluate algorithm performance, that is: distance, number of sources, and subject’s name. Robot will record stereo audio for four seconds after face is detected by system. The recording is then separated by DUET algorithm and produce two source estimations with average computation time 1.8 seconds. With Google API, the recognition accuracy of separated speech is varying between 40%-70%.

Improvement of the histogram in the degenerate unmixing estimation technique algorithm

A method of improving the histogram in the degenerate unmixing estimation technique (DUET) algorithm is proposed. The DUET algorithm is one of the methods of blind signal separation (BSS). The BSS framework is to retrieve source signals from mixtures of them without a priori information about the source signals and the mixing process. In the DUET algorithm, the histogram of both the direction-of-arrivals (DOAs) and the distances is formed from the mixtures which are observed using two sensors. And then, signal separation is achieved using time-frequency masking based on the histogram. Consequently, the capability for the DUET algorithm strongly depends on the performance of the histogram. In general, the histogram is degraded by the reverberation or the reflection of the source signals when the DUET algorithm is applied in the real environment. Our approach is to improve the histogram of the DUET algorithm. In our method, the phase component of the observed mixture at each frequency bin is modified by those at the neighboring frequency bins. The proposed method gives the sharper histogram in comparison with the conventional approach.

Evaluations on underdetermined blind source separation in adverse environments using time-frequency masking

The successful implementation of speech processing systems in the real world depends on its ability to handle adverse acoustic conditions with undesirable factors such as room reverberation and background noise. In this study, an extension to the established multiple sensors degenerate unmixing estimation technique (MENUET) algorithm for blind source separation is proposed based on the fuzzy c-means clustering to yield improvements in separation ability for underdetermined situations using a nonlinear microphone array. However, rather than test the blind source separation ability solely on reverberant conditions, this paper extends this to include a variety of simulated and real-world noisy environments. Results reported encouraging separation ability and improved perceptual quality of the separated sources for such adverse conditions. Not only does this establish this proposed methodology as a credible improvement to the system, but also implies further applicability in areas such as noise suppression in adverse acoustic environments.

Source Separation on Seismic Data: Application in a Geophysical Setting

This article gives a brief description of the degenerate unmixing estimation technique (DUET) and applies it in a geophysical setting. Source separation has not been fully addressed by geophysicists and is a crucial first step to locating simultaneous sources, which in turn helps with understanding the dynamics of the sources and their source mechanisms. DUET is applied to synthetic seismic signals. The source separation method works success fully to separate two contemporary explosive sources, and two simultaneous oblique tensile cracks in a three-dimensional (3-D) structural model of Mount Etna. The method has also been applied to field recordings on Mount Etna since 2008. The method separates long-period (LP) events from tremor, LP events from volcano tectonic (VT) events, and different sources of tremor from each other.

A time-frequency blind separation method for underdetermined speech mixtures

The proposed Blind Source Separation method (BSS), based on sparse representations, fuses time-frequency analysis and the clustering approach to separate underdetermined speech mixtures in the anechoic case regardless of the number of sources. The method remedies the insufficiency of the Degenerate Unmixing Estimation Technique (DUET) which assumes the number of sources a priori. In the proposed algorithm, the Short-Time Fourier Transform (STFT) is used to obtain the sparse representations, a clustering method called Unsupervised Robust C-Prototypes (URCP) which can accurately identify multiple clusters regardless of the number of them is adopted to replace the histogram-based technique in DUET, and the binary time-frequency masks are constructed to separate the mixtures. Experimental results indicate that the proposed method results in a substantial increase in the average Signal-to-Interference Ratio (SIR), and maintains good speech quality in the separation results.

Fractional Delay Estimation for Blind Source Separation and Localization of Temporomandibular Joint Sounds

Temporomandibular joint (TMJ) sound sources are generated from the two joints connecting the lower jaw to the temporal bone. Such sounds are important diagnostic signs in patients suffering from temporomandibular disorder (TMD). In this study, we address the problem of source separation of the TMJ sounds. In particular, we examine patients with only one TMJ generating "clicks". Thereafter, we consider the TMJ sounds recorded from the two auditory canals as mixtures of clicks from the TMD joint and the noise produced by the other healthy/normal TMJ. We next exploit the statistical nonstationary nature of the TMJ signals by employing the degenerate unmixing estimation technique (DUET) algorithm, a time-frequency (T-F) approach to separate the sources. As the DUET algorithm requires the sensors to be closely spaced, which is not satisfied by our recording setup, we have to estimate the delay between the recorded TMJ sounds to perform an alignment of the mixtures. Thus, the proposed extension of DUET enables an essentially arbitrary separation of the sensors. It is also shown that DUET outperforms the convolutive Infomax algorithm in this particular TMJ source separation scenario. The spectra of both separated TMJ sources with our method are comparable to those available in existing literature. Examination of both spectra suggests that the click source has a better audible prominence than the healthy TMJ source. Furthermore, we address the problem of source localization. This can be achieved automatically by detecting the sign of our proposed mutual information estimator which exhibits a maximum at the delay between the two mixtures. As a result, the localized separated TMJ sources can be of great clinical value to dental specialists.

시간-주파수 영역에서의 스테레오 사운드 분리기법

본 논문은 스테레오 사운드에서 합쳐지기 이전의 개별적인 사운드를 분리해내는 기법을 제안한다. 기존의 Degenerate Unmixing Estimation Technique (DUET) 알고리즘의 W-Disjoint Orthogonal 가정에 기반을 두고 있으며, Windowed-Fourier 변환을 사용하여 시간-주파수 영역에서 주요 프로세스를 수행한다. 제안된 방식은 패닝 인덱스의 거리차이에 따라 가중치를 준 마스크를 사용하는 기법과 양쪽 채널의 성분을 비교하여 바이너리 기반의 마스크를 사용하는 방식이다. 전자는 부드러운 분리 특성을 보여주며, 후자는 높은 분리 특성을 보여주었다. 마지막에 실험을 통해 기존의 방식과 제안된 방식을 비교함으로써, 제안된 방식이 기존 방식 보다 좋은 성능을 가지고 있음을 알아볼 것이다. This paper presents a new demixing method that separates each source from a stereo sound mixture. Under the W-Disjoint Orthogonal assumption in DUET(Degenerate Unmixing Estimation Technique) algorithm. The proposed method is mainly processed in time-frequency domain by using windowed-fourier transform. In this paper there are two main contributions: a weighted mask by panning index distances and a binary mask by comparing each channel value. The former has tender demixing characteristic, and the latter has stronger demixing characteristic. In experimental results, we will show that both masks produce more robust demixing than the existing demixing methods do.

Underdetermined Anechoic Blind Source Separation via $\ell^{q}$-Basis-Pursuit With $q≪1$

In this paper, we address the problem of underdetermined blind source separation (BSS) of anechoic speech mixtures. We propose a demixing algorithm that exploits the sparsity of certain time-frequency expansions of speech signals. Our algorithm merges lscrq -basis-pursuit with ideas based on the degenerate unmixing estimation technique (DUET) [Yiotalmaz and Rickard, Blind Source Separation of Speech Mixtures via Time-Frequency Masking, IEEE Transactions on Signal Processing, vol. 52, no. 7, pp. 1830-1847, July 2004]. There are two main novel components to our approach: 1, our algorithm makes use of all available mixtures in the anechoic scenario where both attenuations and arrival delays between sensors are considered, without imposing any structure on the microphone positions, and 2, we illustrate experimentally that the separation performance is improved when one uses lscrq-basis-pursuit with q < 1 compared to the q = 1 case. Moreover, we provide a probabilistic interpretation of the proposed algorithm that explains why a choice of 0.1 les q les 0.4 is appropriate in the case of speech. Experimental results on both simulated and real data demonstrate significant gains in separation performance when compared to other state-of-the-art BSS algorithms reported in the literature.