In this letter, we develop a multichannel blind source separation algorithm based on a beamspace transform and the multichannel nonnegative matrix factorization (NMF) method. The conventional multichannel NMF algorithm performs well with multichannel mixing data, but there is still room for enhancement in multichannel real-world recording data. In this letter, we consider a beamspace-time-frequency domain data model for multichannel NMF method, and enhance the conventional method using a beamspace transform. Our decomposition algorithm is applied to 2-channel and 4-channel unsupervised audio source separation, using a dataset from the international Signal Separation Evaluation Campaign 2010 (SiSEC 2010). Our algorithm shows a better performance than the conventional NMF method in an evaluation results.

In this letter, reduced complexity self-tuning algorithms are proposed using simplified parameter updating procedures. Convergence analysis based on the independence assumption and the ordinary differential equation (ODE) method shows that the tuning parameter of the proposed algorithm attains the same limit as the conventional self-tuning adaptive algorithm. Simulations are carried out for channel estimation to support the analysis and performance of the proposed algorithms.

A theoretical basis for optimal multichannel speech enhancementis presented, sufficient, flexible to be used with any assumed statistical model and optimality criterion. Any Bayesian optimal one-channel estimator for speech enhancement can be generalized to the multichannel case as a sequentially constructed minimum variance distortionless response (MVDR) beamformer followed by an optimal one-channel postfilter. We present experimental results using the minimum mean-square error log-spectral amplitude (MMSE-logSA) optimality criterion, applied to a statistical model with simplified channel but realistic inter-microphone noise coherence. Word error rate in the audio-visual speech in a car (AVICAR) corpus (moving car, windows open) is reduced from 18% to 9%.

A novel blind widely linear (WL) minimum output energy (MOE) algorithm is proposed for the CDMA receiver. Whereas the conventional WL algorithms are only applicable to real-valued modulation, the proposed receiver is applicable to complex-valued modulation. The blind WL-MOE filter is analyzed, and the convergence properties of an adaptive implementation are derived. Simulation results confirm that the proposed algorithm has higher steady-state SINR and faster convergence speed than the conventional MOE algorithm but cannot outperform the conventional WL-MOE due to severer constraints.

We experience the coloring of reproduced sound in an automotive space in a different way than in a large space such as a concert hall. It comes from the well-separated acoustic modes in the low frequency range up to a relatively high crossover frequency. The unwanted sound coloring can be reduced by equalization. However, this is not a simple matter because the binaural response is different in each person and drivers are likely to move their heads while driving. We introduce a novel approach, based on minimum phase inversion, for the equalization of the low frequency response to compensate the coloring. We compare the proposed approach with the conventional least square based inversion and show the superiority of our approach by experiment. This is confirmed by the results of listening tests.

In this letter, we present generalized encoding and decoding functions for an ambisonic system. Our approach provides matrix representations for optimum encoding and decoding for both odd and even number of N loudspeakers. Particularly, we present ambisonic components (signals or channels) for even N and a parameterized decoding equation for the optimum localization.

This paper discusses a DOA algorithm for UAV(Unmanned Aerial Vehicle) auto landing. In UAV auto landing with radar system, ground multi path effect and time varying DOA make it difficult to estimate angle information of UAV. This paper proposed a new algorithm based on forward-backward Kalman filter with time varying forgetting factor. This algorithm effectively handles the multipath effect and time varying DOA environments to estimate highly accurate DOA.

The research for the jammer cancellation problem in sonar or radar system has been studied and a large number of results for the spatially fixed jammer case have been represented. However, the performances of the conventional array techniques are severely degraded for the moving interfering sources. Such degradations occur because the system cannot promptly adapt to the movements of the interferers. Several adaptive methods have been developed and made up for this disadvantage, but they also have some restrictions on the number of sensors or samples to be referred. In this paper, we present a new adaptive beamforming algorithm with variable forgetting factor for the moving jammer environments. The important advantage using the variable forgetting factor is that the notches of the adaptive pattern can maintain the sharpness and rapidly cope with the changing jamming situation. The computer simulations show that the proposed algorithm provides the noticeable performance improvements in terms of output signal to interference plus noise ratio (SINR), compared with traditional adaptive beamforming schemes.

We consider the problem of joint equalization and decoding, using the method of turbo equalization originally developed by Douillard, et al. [3]. In its original form, turbo-equalization requires accurate knowledge of the channel at the receiver. We propose a receiver structure, based on a soft-input Kalman channel estimator, that can operate effectively without accurate channel knowledge and without training data. The resulting joint channel and data estimator is shown to outperform standard turbo equalization based on moderate-length training data.

Variable forgetting factor linear least squares (VFFLLS) algorithm is presented in order to improve the tracking capability of the channel estimation. Compared to the already-existing algorithm-exponentially windowed recursive least squares (EW-RLS) with the optimal forgetting factor, this method makes remarkable improvement in a fast fading environment. The effects of channel parameter-signal to noise ratio, fading rate, are tested in computer simulations with the given channel model. The performance of each algorithm is assessed in terms of the mean-square-identification-error (MSIE).

A mixture interacting multiple model (MIMM) algorithm is proposed to enhance speech contaminated by additive nonstationary noise. In this approach, a mixture hidden filter model (HFM) is used for clean speech modeling and a single hidden filter is used for noise process modeling. The MIMM algorithm gives better enhancement results than the IMM algorithm. The results show that the proposed method offers performance gain compared to the previous results in with slightly increased complexity.

In this letter, we present a fast encoding algorithm for vector quantization that uses two characteristics of a vector, mean, and variance. Although a similar method using these features was already proposed, it handles these features separately, On the other hand, the proposed algorithm utilizes these features simultaneously to save computation time all the more. Since the proposed algorithm rejects those codewords that are impossible to be the nearest codeword, it produces the same output as the conventional full search algorithm. The simulation results confirm the effectiveness of the proposed algorithm.

The K-means algorithm is widely used in vector quantizer (VQ) design and clustering analysis. In VQ context, this algorithm iteratively updates an initial codebook and converges to a locally optimal codebook in certain conditions. It iteratively satisfies each of the two necessary conditions for an optimal quantizer; the nearest neighbor condition for the partition and centroid condition for the codevectors. In this letter, we propose a new algorithm for both vector quantizer design and clustering analysis as an alternative to the conventional K-means algorithm. The algorithm is almost the same as the K-means algorithm except for a modification at codebook updating step. It does not satisfy the centroid condition iteratively, but asymptotically satisfies it as the number of iterations increases. Experimental results show that the algorithm converges to a better locally optimal codebook with an accelerated convergence speed.