This paper proposes a new angular measurement system to a moving target in the presence of clutter. We apply MUSIC (MUltiple SIgnal Classification) to the outputs of a Doppler filter bank consisting of quadrature mirror filter (QMF). The comparison between QMF and the short time Fourier transform (STFT) as a preprocessor of MUSIC is also discussed. DOA estimation performance by QMF-MUSIC is nearly equal to that of STFT-MUSIC. On the other hand, QMF-MUSIC overcomes STFT-MUSIC in the aspect of computational cost. In a specific example in this paper, the proposal QMF bank by Daubechies (4th order) wavelet requires 80% fewer the number of multiplications and 25% fewer the number of additions than the FFT-based STFT filter bank.

This paper presents a novel algorithm for spectral subtraction (SS). The method is derived from a relation between the spectrum obtained by the discrete Fourier transform (DFT) and that by a subspace decomposition method. By using the relation, it is shown that a noise reduction algorithm based on subspace decomposition is led to an SS method in which noise components in an observed signal are eliminated by subtracting variance of noise process in the frequency domain. Moreover, it is shown that the method can significantly reduce computational complexity in comparison with the method based on the standard subspace decomposition. In a similar manner to the conventional SS methods, our method also exploits the variance of noise process estimated from a preceding segment where speech is absent, whereas the noise is present. In order to more reliably detect such non-speech segments, a novel robust voice activity detector (VAD) is then proposed. The VAD utilizes the spread of eigenvalues of an autocorrelation matrix corresponding to the observed signal. Simulation results show that the proposed method yields an improved enhancement quality in comparison with the conventional SS based schemes.

DOA (Direction Of Arrival) estimation is a useful technique in various positioning applications including the DOA-based adaptive array antenna system. This paper presents a practical implementation of FPGA (Field Programmable Gate Array) based fast DOA estimator for wireless cellular basestation. This system incorporates spectral unitary MUSIC (MUltiple SIgnal Classification) algorithm, which is one of the representative super resolution DOA estimation techniques. This paper proposes a way of digital signal processor design suitable for FPGA and its real hardware implementation. In this system, all digital signal processing procedures are computed by the only fixed-point operation with finite word-length for fast processing and low power consumption. The performance will be assessed by hardware level simulations and experiments in a radio anechoic chamber.

A new algorithm, termed reduced-order Root-MUSIC, for high resolution direction finding is proposed. It requires finding all the roots of a polynomial with an order equaling twice the number of propagating signals. Some Monte Carlo simulations are used to test the effectiveness of the proposed algorithm.

In this paper the correlation spectrum of antenna array is introduced. Based on the relationship between the correlation spectrum and space spectrum of MUSIC, we proposed a novel approach to improve the DOA estimation by arranging the linear antenna array elements using genetic algorithm (GA) in optimizing the correlation spectrum. The DOA estimation performance of the optimized array is validated by Monte Carlo simulation and Cramer-Rao bound (CRB), which are improved compared with that of the traditional uniform linear array and the Minimum-Redundancy array (MRA).

This paper first discusses the misinterpretation of the concept of "ubiquitous computing" that Mark Weiser originally proposed in 1991. Weiser's main message was not the ubiquity of computers, but the transparency of interface that determines users' perception of digital technologies embedded in our physical environment seamlessly. To explore Weiser's philosophy of transparency in interfaces, this paper presents the design of an interface that uses glass bottles as "containers" and "controls" for digital information. The metaphor is a perfume bottle: Instead of scent, the bottles have been filled with music -- classical, jazz, and techno music. Opening each bottle releases the sound of a specific instrument accompanied by dynamic colored light. Physical manipulation of the bottles -- opening and closing -- is the primary mode of interaction for controlling their musical contents. The bottles illustrates Mark Weiser's vision of the transparent (or invisible) interface that weaves itself into the fabric of everyday life. The bottles also exploits the emotional aspects of glass bottles that are tangible and visual, and evoke the smell of perfume and the taste of exotic beverages. This paper describes the design goals of the bottle interface, the arrangement of musical content, the implementation of the wireless electromagnetic tag technology, and the feedback from users who have played with the system.

The objective of this research is to compare the performance of the Matrix Pencil Method (MPM) and well known root-MUSIC algorithm for high resolution DOA estimation. Performance of each technique in terms of the probability of resolution and SNR in the presence of noise is investigated. Simulation results show that the MPM has a superior resolution to the root-MUSIC algorithm.

In this paper, a preprocessing approach to improving the quality of the music on a mobile phone using Enhanced variable rate codec (EVRC) is presented. Our approach works well on music signals considerably reducing the number of time-clipped frames.

This paper describes a method of analyzing musical sound using a self-organizing map. To take compound factors into account, energy spectra whose frequency ranges were based on the psycho-acoustic experiments were used as input data. Results for music compact discs confirmed that our method could effectively display the positioning and relationships among musical sounds on a map.

The cardio-music synchrogram, which is a visualization tool that helps us observe the phase synchronization between heartbeats and beats of music, is originated in cardio-respiratory synchrogram. In this paper, we proposed a novel statistical method for detecting synchronization periods in cardio-music synchrogram (CMS), and reported the synchronization periods of the cardiac activity for 8 subjects in a listening experiments. Our approach focused on the difference between the statistical distributions of the correlation coefficient calculated for the CMS in a control experiment and in the CMS in the listening experiment.

Music style is one of the features that people used to classify music. Discovery of music style is helpful for the design of content-based music retrieval systems. In this paper we investigated the mining and classification of music style by melody from a collection of MIDI music. We extracted the chord from the melody and investigated the representation of extracted features and corresponding mining techniques for music classification. Experimental results show that the classification achieved 64% to 84% accuracy for two-way classification.

This paper studies on a fast approach for the eigenproblems of correlation matrices used in direction-of-arrival (DOA) estimation algorithms, especially for the case that the number of arriving waves is a few. The eigenvalues and the corresponding eigenvectors can be obtained in a very short time by the algebraic solvent of up to quartic polynomials. We also confirm that the present approach does not make the accuracy worse when it is implemented by finite word-length processors like digital signal processor (DSP) or field programmable gate array (FPGA).

This paper describes a rhythm pattern accuracy diagnosis system based on the rhythm pattern matching algorithm and a diagnosis feedback method by employing the SVM technique. A beat rhythm pattern is recorded by a PC and analyzed with an algorithm including cluster-analysis-based pattern matching. Rhythm performance is represented by a performance feature vector, which features note length deviation, note length instability, and tempo instability. The performance feature vector is effective for objectively evaluating the accuracy of rhythm patterns objectively. In addition, this system has the music experts' knowledge base, which is calculated from the performance feature vectors associated with the experts' subjective evaluation by listening to the performance. The system generates both an objective measuring report, and experts' comments for learners. Reproductivity of experts' comments is statistically indicated to be excellent for eight rhythm patterns, two tempo levels, and eight users. Reliability of experts' comments are also described considering the threshold of the decision function of SVM. Subjective evaluation of the system is carried out by fifteen users by a questionnaire using the SD method. As a result of factor analysis for the sixteen questions, four factors named "Audio-visual representation," "User-friendliness," "Reliability," and "Window representation," are extracted. Users' four factor scores indicate that the system is reliable and easy to use.

Computing the Eigen Value Decomposition (EVD) of a symmetric matrix is a frequently encountered problem in adaptive (or smart or software) antenna signal processing, for example, super resolution DOA (Direction Of Arrival) estimation algorithms such as MUSIC (MUltiple SIgnal Classification) and ESPRIT (Estimation of Signal Parameters via Rotational Invariance Technique). In this paper the hardware architecture of the fast EVD processor of symmetric correlation matrices for the application of an adaptive antenna technology such as DOA estimation is proposed and the basic idea is also presented. Cyclic Jacobi method is well known for the simplest algorithm and easily implemented but its convergence time is slower than other factorization algorithm like QR-method. But if considering the fast parallel computation of the EVD with a hardware architecture like ASIC (Application Specific Integrated Circuit) or FPGA (Field Programmable Gate Array), the Jacobi method can be a appropriate solution, since it offers a quite higher degree of parallelism and easier implementation than other factorization algorithms. This paper computes the EVD using a Jacobi-type method, where the vector rotations and the angles of the rotations are obtained by CORDIC (COordinate Rotation DIgital Computer). The hardware architecture suitable for ASIC or FPGA with fixed-point arithmetic is presented. Because it consists of only shift and add operations, this hardware friendly feature provides easy and efficient implementation. In this paper, the computational load, the estimate of circuit scale and expected performance are discussed and the validation of fixed-point arithmetic for the practical application to MUSIC DOA estimation is examined.

The problem of the super-resolution time delay estimation of the real stationary signals is addressed in this paper. The time delay estimation is first converted into a frequency estimation problem. Then a MUSIC-type algorithm to estimate the subsequent frequency from the single-experiment data is proposed, which not only avoids the mathematical model mismatching but also utilizes the advantages of the subspace-based methods. The mean square errors (MSEs) of the time delay estimate of the MUSIC-type method for varying signal-to-noise (SNR) and separation of two received signal components are shown to illustrate that they approximately coincide with the corresponding Cramer-Rao bound (CRB). Finally, the comparison between the MUSIC-type method and the other conventional methods is presented to show the advantages of the proposed method in this paper.

This paper addresses the single channel speech enhancement method which utilizes the mean value and variance of the logarithmic noise power spectra. An important issue for single channel speech enhancement algorithm is to determine the trade-off point for the spectral distortion and residual noise. Thus the accurate discrimination between speech spectral and noise components is required. The conventional methods determine the trade-off point using parameters obtained experimentally. As a result spectral discrimination is not adequate. And the enhanced speech is deteriorated by spectral distortion or residual noise. Therefore, a criteria to determine the point is necessary. The proposed method determines the trade-off point of spectral distortion and residual noise level by discrimination between speech spectral and noise components based on statistical criteria. The spectral discrimination is performed using hypothesis testing that utilizes means and variances of the logarithmic power spectra. The discriminated spectral components are divided into speech-dominant spectral components and noise-dominant ones. For the speech-dominant ones, spectral subtraction is performed to minimize the spectral distortion. For the noise-dominant ones, attenuation is performed to reduce the noise level. The performance of the method is confirmed in terms of waveform, spectrogram, noise reduction level and speech recognition task. As a result, the noise reduction level and speech recognition rate are improved so that the method reduces the musical noise effectively and improves the enhanced speech quality.

It is known that MUSIC and ESPRIT algorithms can estimate simultaneously both the instantaneous Direction of Arrival (DOA) and the instantaneous Angular Spread (AS) in multiple scattering environments. These algorithms use the Extended Array Mode Vector (EAMV) with complex angle. The previous work evaluated the performance of those algorithms by comparing the estimated DOA and the estimated AS with the DOA and the AS given in the EAMV, which uses the first-order approximation. Thus, this evaluation method has not clearly reflected the estimation accuracy of MUSIC and ESPRIT. This paper presents the joint estimation performance of MUSIC and ESPRIT by introducing the criteria for evaluation. For this, the spatial signature (SS) is reconstructed from the estimates of the DOA and the AS, and compared to the true SS in the meaning of data fitting.

In this paper, we propose a method to reconstruct current distributions in the human brain from neuromagnetic measurements. The proposed method is based on the weighted lead-field synthetic (WLFS) filtering technique with the weighting factors calculated from the results of previous source space scanning. In this method, in addition to the depth normalization technique, weighting factors of the WLFS are determined by the cost values previously calculated based on the multiple signal classification (MUSIC) scan. We performed computer simulations of this method under noisy measurement conditions and compared the results to those obtained with the conventional WLFS method. The results of the simulations indicate that the proposed method is effective for the reconstruction of the current distributions in the human brain using magnetoencephalographic (MEG) measurements, even if the signal-to-noise ratio of the measured data is relatively low. We applied the proposed method to the magnetoencephalographic data obtained during a mental image processing task that included object recognition and mental rotation operations. The results suggest that the proposed method can extract the neural activity in the extrastriate visual region and the parietal region. These results are in agreement with the results of previous positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) studies.

In recent years, several inverse solutions of magnetoencephalography (MEG) have been proposed. Among them, the multiple signal classification (MUSIC) method utilizes spatio-temporal information obtained from magnetic fields. The conventional MUSIC method is, however, sensitive to Gaussian noise and a sufficiently large signal-to-noise ratio (SNR) is required to estimate the number of sources and to specify the precise locations of electrical neural activities. In this paper, a new algorithm for solving the inverse problem using the fourth order MUSIC (FO-MUSIC) method is proposed. We apply it to the MEG source estimation problem. Numerical simulations demonstrate that the proposed FO-MUSIC algorithm is more robust against Gaussian noise than the conventional MUSIC algorithm.

This paper describes performance enhancement of high-resolved delay time estimation by pattern diversity of wheel-shaped dipole antennas for the MUSIC algorithm. We propose that the wheel-shaped dipole antennas are used to average covariance matrices weightedly with their pattern diversity without decreasing the matrix dimension and to obtain freedom of selecting sweeping frequencies. It is numerically confirmed that the wheel-shaped dipole antennas can be used to enhance capability of the delay time estimation.