Vaninirappuputhenpurayil Gopalan Reju

An algorithm for mixing matrix estimation in instantaneous blind source separation

Sparsity of signals in the frequency domain is widely used for blind source separation (BSS) when the number of sources is more than the number of mixtures (underdetermined BSS). In this paper we propose a simple algorithm for detection of points in the time-frequency (TF) plane of the instantaneous mixtures where only single source contributions occur. Samples at these points in the TF plane can be used for the mixing matrix estimation. The proposed algorithm identifies the single-source-points (SSPs) by comparing the absolute directions of the real and imaginary parts of the Fourier transform coefficient vectors of the mixed signals. Finally, the SSPs so obtained are clustered using the hierarchical clustering algorithm for the estimation of the mixing matrix. The proposed idea for the SSP identification is simpler than the previously reported algorithms.

Underdetermined Convolutive Blind Source Separation via Time–Frequency Masking

In this paper, we consider the problem of separation of unknown number of sources from their underdetermined convolutive mixtures via time-frequency (TF) masking. We propose two algorithms, one for the estimation of the masks which are to be applied to the mixture in the TF domain for the separation of signals in the frequency domain, and the other for solving the permutation problem. The algorithm for mask estimation is based on the concept of angles in complex vector space. Unlike the previously reported methods, the algorithm does not require any estimation of the mixing matrix or the source positions for mask estimation. The algorithm clusters the mixture samples in the TF domain based on the Hermitian angle between the sample vector and a reference vector using the well known k -means or fuzzy c -means clustering algorithms. The membership functions so obtained from the clustering algorithms are directly used as the masks. The algorithm for solving the permutation problem clusters the estimated masks by using k-means clustering of small groups of nearby masks with overlap. The effectiveness of the algorithm in separating the sources, including collinear sources, from their underdetermined convolutive mixtures obtained in a real room environment, is demonstrated.

Convolution Using Discrete Sine and Cosine Transforms

In this paper, we derive a relation for the circular convolution operation in the discrete sine and cosine transform domains. The transform coefficients are either symmetric or asymmetric and hence we need to calculate only half of the total coefficients. Since fast algorithms are available for the computation of discrete sine and cosine transforms, the proposed method is an alternative to the discrete Fourier transform method for filtering applications.

Localization of Taps on Solid Surfaces for Human-Computer Touch Interfaces

Localization of impacts on solid surfaces is a challenging task due to dispersion where the velocity of wave propagation is frequency dependent. In this work, we develop a source localization algorithm on solids with applications to human-computer interface. We employ surface-mounted piezoelectric shock sensors that, in turn, allow us to convert existing flat surfaces to a low-cost touch interface. The algorithm estimates the time-differences-of-arrival between the signals via onset detection in the time-frequency domain. The proposed algorithm is suitable for vibration signals generated by a metal stylus and a finger. The validity of the algorithm is then verified on an aluminium and a glass plate surface.

Partial separation method for solving permutation problem in frequency domain blind source separation of speech signals

This paper addresses the well known permutation problem in frequency domain blind source separation. The proposed method uses correlation between two signals in each DFT bin to solve the permutation problem. One of the signals is partially separated by a time domain blind source separation method and the other is obtained by the frequency domain blind source separation method. Two different ways of configuring the time and frequency domain blocks, i.e., in parallel or cascade, have been studied. The cascaded configuration not only achieves a better separation performance but also reduces the computational cost as compared to the parallel configuration.

A Linear Source Recovery Method for Underdetermined Mixtures of Uncorrelated AR-Model Signals Without Sparseness

Conventional sparseness-based approaches for instantaneous underdetermined blind source separation (UBSS) do not take into account the temporal structure of the source signals. In this work, we exploit the source temporal structure and propose a linear source recovery solution for the UBSS problem which does not require the source signals to be sparse. Assuming the source signals are uncorrelated and can be modeled by an autoregressive (AR) model, the proposed algorithm is able to estimate the source AR coefficients from the mixtures given the mixing matrix. We prove that the UBSS problem can be converted into a determined problem by combining the source AR model together with the original mixing equation to form a state-space model. The Kalman filter is then applied to obtain a linear source estimate in the minimum mean-squared error sense. Simulation results using both synthetic AR signals and speech utterances show that the proposed algorithm achieves better separation performance compared with conventional sparseness-based UBSS algorithms.

A Robust Correlation Method for Solving Permutation Problem in Frequency Domain Blind Source Separation of Speech Signals

This paper addresses the well-known permutation problem, inconsistent permutation in the discrete Fourier transform bins after independent component analysis, in the frequency domain blind source separation. The method which utilizes the correlation between the adjacent bins in speech signals is popular among the techniques for solving permutation problem. However, the reliability of the method is very low. This paper presents a robust correlation method for solving the permutation problem, which utilizes the correlation between the signals in each DFT bin, one of which is partially separated by a time domain BSS method and the other is obtained by a frequency domain BSS method. The proposed method showed almost the same separation performance as that of the method which uses the correlation between adjacent bins and is highly reliable at the same time

A GMM Post-Filter for Residual Crosstalk Suppression in Blind Source Separation

Existing algorithms employ the Wiener filter to suppress residual crosstalk in the outputs of blind source separation algorithms. We show that, in the context of BSS, the Wiener filter is optimal in the maximum likelihood (ML) sense only for normally-distributed signals. We then propose to model the distribution of speech signals using the Gaussian mixture model (GMM) and then derive a post-filter in the ML sense using the expectation-maximization algorithm. We show that the GMM introduces a probabilistic sample weight that is able to emphasize speech segments that are free of crosstalk components in the BSS output and this results in a better estimate of the post-filter. Simulation results show that the proposed post-filter achieves better crosstalk suppression than the Wiener filter for BSS.

Source localization on solids utilizing logistic modeling of energy transition in vibration signals

We propose a new algorithm for source localization on rigid surfaces, which allows one to convert daily objects into human-computer touch interfaces using surface-mounted vibration sensors. This is achieved via estimating the time-difference-of-arrivals (TDOA) of the signals across the sensors. In this work, we employ a smooth parametrized function to model the gradual noise-to-signal energy transition at each sensor. Specifically, the noise-to-signal transition is modeled by a four-parameter logistic function. The TDOA is then estimated as the difference in time shifts of the functions fitted to the sensor data. Experiment results show that the proposed algorithm significantly outperforms existing techniques which adopt the abrupt change model for time-of-arrival estimation.

Multi-source direction-of-arrival estimation in a reverberant environment using single acoustic vector sensor

We address the problem of estimating direction-of-arrivals (DOAs) for multiple sound sources using a single acoustic vector sensor (AVS) in an enclosed room environment. It is well-known that multi-source DOA estimation in an enclosed environment is challenging due to room reverberation, environmental noise and overlapping of the source spectra. In this work, we propose a multi-source DOA estimation algorithm which exploits co-location of the sensor elements in AVS. We identify time-frequency (TF) zones of the received signals in which only one source is dominant with a high signal-to-reverberation ratio. DOA estimation is then achieved via the use of clustering of the Hermitian angle feature. Simulation results show that the proposed DOA estimation algorithm is robust to both reverberation and environmental noise.