Showing papers on "Spectral density estimation published in 2009"

Distant Speech Recognition

Abstract: Foreword. Preface. 1 Introduction. 1.1 Research and Applications in Academia and Industry. 1.2 Challenges in Distant Speech Recognition. 1.3 System Evaluation. 1.4 Fields of Speech Recognition. 1.5 Robust Perception. 1.6 Organizations, Conferences and Journals. 1.7 Useful Tools, Data Resources and Evaluation Campaigns. 1.8 Organization of this Book. 1.9 Principal Symbols used Throughout the Book. 1.10 Units used Throughout the Book. 2 Acoustics. 2.1 Physical Aspect of Sound. 2.2 Speech Signals. 2.3 Human Perception of Sound. 2.4 The Acoustic Environment. 2.5 Recording Techniques and Sensor Configuration. 2.6 Summary and Further Reading. 2.7 Principal Symbols. 3 Signal Processing and Filtering Techniques. 3.1 Linear Time-Invariant Systems. 3.2 The Discrete Fourier Transform. 3.3 Short-Time Fourier Transform. 3.4 Summary and Further Reading. 3.5 Principal Symbols. 4 Bayesian Filters. 4.1 Sequential Bayesian Estimation. 4.2 Wiener Filter. 4.3 Kalman Filter and Variations. 4.4 Particle Filters. 4.5 Summary and Further Reading. 4.6 Principal Symbols. 5 Speech Feature Extraction. 5.1 Short-Time Spectral Analysis. 5.2 Perceptually Motivated Representation. 5.3 Spectral Estimation and Analysis. 5.4 Cepstral Processing. 5.5 Comparison between Mel Frequency, Perceptual LP and warped MVDR Cepstral Coefficient Frontends. 5.6 Feature Augmentation. 5.7 Feature Reduction. 5.8 Feature-Space Minimum Phone Error. 5.9 Summary and Further Reading. 5.10 Principal Symbols. 6 Speech Feature Enhancement. 6.1 Noise and Reverberation in Various Domains. 6.2 Two Principal Approaches. 6.3 Direct Speech Feature Enhancement. 6.4 Schematics of Indirect Speech Feature Enhancement. 6.5 Estimating Additive Distortion. 6.6 Estimating Convolutional Distortion. 6.7 Distortion Evolution. 6.8 Distortion Evaluation. 6.9 Distortion Compensation. 6.10 Joint Estimation of Additive and Convolutional Distortions. 6.11 Observation Uncertainty. 6.12 Summary and Further Reading. 6.13 Principal Symbols. 7 Search: Finding the Best Word Hypothesis. 7.1 Fundamentals of Search. 7.2 Weighted Finite-State Transducers. 7.3 Knowledge Sources. 7.4 Fast On-the-Fly Composition. 7.5 Word and Lattice Combination. 7.6 Summary and Further Reading. 7.7 Principal Symbols. 8 Hidden Markov Model Parameter Estimation. 8.1 Maximum Likelihood Parameter Estimation. 8.2 Discriminative Parameter Estimation. 8.3 Summary and Further Reading. 8.4 Principal Symbols. 9 Feature and Model Transformation. 9.1 Feature Transformation Techniques. 9.2 Model Transformation Techniques. 9.3 Acoustic Model Combination. 9.4 Summary and Further Reading. 9.5 Principal Symbols. 10 Speaker Localization and Tracking. 10.1 Conventional Techniques. 10.2 Speaker Tracking with the Kalman Filter. 10.3 Tracking Multiple Simultaneous Speakers. 10.4 Audio-Visual Speaker Tracking. 10.5 Speaker Tracking with the Particle Filter. 10.6 Summary and Further Reading. 10.7 Principal Symbols. 11 Digital Filter Banks. 11.1 Uniform Discrete Fourier Transform Filter Banks. 11.2 Polyphase Implementation. 11.3 Decimation and Expansion. 11.4 Noble Identities. 11.5 Nyquist( M ) Filters. 11.6 Filter Bank Design of De Haan et al . 11.7 Filter Bank Design with the Nyquist( M ) Criterion. 11.8 Quality Assessment of Filter Bank Prototypes. 11.9 Summary and Further Reading. 11.10 Principal Symbols. 12 Blind Source Separation. 12.1 Channel Quality and Selection. 12.2 Independent Component Analysis. 12.3 BSS Algorithms based on Second-Order Statistics. 12.4 Summary and Further Reading. 12.5 Principal Symbols. 13 Beamforming. 13.1 Beamforming Fundamentals. 13.2 Beamforming Performance Measures. 13.3 Conventional Beamforming Algorithms. 13.4 Recursive Algorithms. 13.5 Nonconventional Beamforming Algorithms. 13.6 Array Shape Calibration. 13.7 Summary and Further Reading. 13.8 Principal Symbols. 14 Hands On. 14.1 Example Room Configurations. 14.2 Automatic Speech Recognition Engines. 14.3 Word Error Rate. 14.4 Single-Channel Feature Enhancement Experiments. 14.5 Acoustic Speaker-Tracking Experiments. 14.6 Audio-Video Speaker-Tracking Experiments. 14.7 Speaker-Tracking Performance vs Word Error Rate. 14.8 Single-Speaker Beamforming Experiments. 14.9 Speech Separation Experiments. 14.10 Filter Bank Experiments. 14.11 Summary and Further Reading. Appendices. A List of Abbreviations. B Useful Background. B.1 Discrete Cosine Transform. B.2 Matrix Inversion Lemma. B.3 Cholesky Decomposition. B.4 Distance Measures. B.5 Super-Gaussian Probability Density Functions. B.6 Entropy. B.7 Relative Entropy. B.8 Transformation Law of Probabilities. B.9 Cascade of Warping Stages. B.10 Taylor Series. B.11 Correlation and Covariance. B.12 Bessel Functions. B.13 Proof of the Nyquist-Shannon Sampling Theorem. B.14 Proof of Equations (11.31-11.32). B.15 Givens Rotations. B.16 Derivatives with Respect to Complex Vectors. B.17 Perpendicular Projection Operators. Bibliography. Index.

Estimation of Respiratory Rate From Photoplethysmogram Data Using Time–Frequency Spectral Estimation

Abstract: We present a new method that uses the pulse oximeter signal to estimate the respiratory rate. The method uses a recently developed time-frequency spectral estimation method, variable-frequency complex demodulation (VFCDM), to identify frequency modulation (FM) of the photoplethysmogram waveform. This FM has a measurable periodicity, which provides an estimate of the respiration period. We compared the performance of VFCDM to the continuous wavelet transform (CWT) and autoregressive (AR) model approaches. The CWT method also utilizes the respiratory sinus arrhythmia effect as represented by either FM or AM to estimate respiratory rates. Both CWT and AR model methods have been previously shown to provide reasonably good estimates of breathing rates that are in the normal range (12-26 breaths/min). However, to our knowledge, breathing rates higher than 26 breaths/min and the real-time performance of these algorithms are yet to be tested. Our analysis based on 15 healthy subjects reveals that the VFCDM method provides the best results in terms of accuracy (smaller median error), consistency (smaller interquartile range of the median value), and computational efficiency (less than 0.3 s on 1 min of data using a MATLAB implementation) to extract breathing rates that varied from 12-36 breaths/min.

A Fourier transform method for nonparametric estimation of multivariate volatility

Abstract: We provide a nonparametric method for the computation of instantaneous multivariate volatility for continuous semi-martingales, which is based on Fourier analysis. The co-volatility is reconstructed as a stochastic function of time by establishing a connection between the Fourier transform of the prices process and the Fourier transform of the co-volatility process. A nonparametric estimator is derived given a discrete unevenly spaced and asynchronously sampled observations of the asset price processes. The asymptotic properties of the random estimator are studied: namely, consistency in probability uniformly in time and convergence in law to a mixture of Gaussian distributions.

A Unified Approach to Sparse Signal Processing

Abstract: A unified view of sparse signal processing is presented in tutorial form by bringing together various fields. For each of these fields, various algorithms and techniques, which have been developed to leverage sparsity, are described succinctly. The common benefits of significant reduction in sampling rate and processing manipulations are revealed. The key applications of sparse signal processing are sampling, coding, spectral estimation, array processing, component analysis, and multipath channel estimation. In terms of reconstruction algorithms, linkages are made with random sampling, compressed sensing and rate of innovation. The redundancy introduced by channel coding in finite/real Galois fields is then related to sampling with similar reconstruction algorithms. The methods of Prony, Pisarenko, and MUSIC are next discussed for sparse frequency domain representations. Specifically, the relations of the approach of Prony to an annihilating filter and Error Locator Polynomials in coding are emphasized; the Pisarenko and MUSIC methods are further improvements of the Prony method. Such spectral estimation methods is then related to multi-source location and DOA estimation in array processing. The notions of sparse array beamforming and sparse sensor networks are also introduced. Sparsity in unobservable source signals is also shown to facilitate source separation in SCA; the algorithms developed in this area are also widely used in compressed sensing. Finally, the multipath channel estimation problem is shown to have a sparse formulation; algorithms similar to sampling and coding are used to estimate OFDM channels.

Gap filling and noise reduction of unevenly sampled data by means of the Lomb-Scargle periodogram

Abstract: . The Lomb-Scargle periodogram is widely used for the estimation of the power spectral density of unevenly sampled data. A small extension of the algorithm of the Lomb-Scargle periodogram permits the estimation of the phases of the spectral components. The amplitude and phase information is sufficient for the construction of a complex Fourier spectrum. The inverse Fourier transform can be applied to this Fourier spectrum and provides an evenly sampled series (Scargle, 1989). We are testing the proposed reconstruction method by means of artificial time series and real observations of mesospheric ozone, having data gaps and noise. For data gap filling and noise reduction, it is necessary to modify the Fourier spectrum before the inverse Fourier transform is done. The modification can be easily performed by selection of the relevant spectral components which are above a given confidence limit or within a certain frequency range. Examples with time series of lower mesospheric ozone show that the reconstruction method can reproduce steep ozone gradients around sunrise and sunset and superposed planetary wave-like oscillations observed by a ground-based microwave radiometer at Payerne. The importance of gap filling methods for climate change studies is demonstrated by means of long-term series of temperature and water vapor pressure at the Jungfraujoch station where data gaps from another instrument have been inserted before the linear trend is calculated. The results are encouraging but the present reconstruction algorithm is far away from being reliable and robust enough for a serious application.

A Globally Convergent Matricial Algorithm for Multivariate Spectral Estimation

Abstract: In this paper, we first describe a matricial Newton-type algorithm designed to solve the multivariable spectrum approximation problem. We then prove its global convergence. Finally, we apply this approximation procedure to multivariate spectral estimation, and test its effectiveness through simulation. Simulation shows that, in the case of short observation records, this method may provide a valid alternative to standard multivariable identification techniques such as Matlab's PEM and Matlab's N4SID.

The Discrete Fourier Transform, Part 4: Spectral Leakage

Abstract: This paper is part 4 in a series of papers about the Discrete Fourier Transform (DFT) and the Inverse Discrete Fourier Transform (IDFT). The focus of this paper is on spectral leakage. Spectral leakage applies to all forms of DFT, including the FFT (Fast Fourier Transform) and the IFFT (Inverse Fast Fourier Transform). We demonstrate an approach to mitigating spectral leakage based on windowing. Windowing temporally isolates the Short-Time Fourier Transform (STFT) in order to amplitude modulate the input signal. This requires that we know the extent, of the event in the input signal and that we have enough samples to yield a sufficient spectral resolution for our application. This report is a part of project Fenestratus, from the skunk-works of DocJava, Inc. Fenestratus comes from the Latin and means "to furnish with windows".

Development of a non-uniform discrete Fourier transform based high speed spectral domain optical coherence tomography system

Abstract: We develop a high speed spectral domain optical coherence tomography (SD-OCT) system based on a custom-built spectrometer and non-uniform discrete Fourier transform (NDFT) to realize minimized depth dependent sensitivity fall-off. After precise spectral calibration of the spectrometer, NDFT of the acquired spectral data is adopted for image reconstruction. The spectrometer is able to measure a wavelength range of about 138nm with a spectral resolution of 0.0674nm at central wavelength of 835nm, corresponding to an axial imaging range of 2.56mm in air. Zemax simulations and sensitivity fall-off measurements under two alignment states of the spectrometer are given. Both theoretical simulations and experiments are done to study the depth dependent sensitivity of the developed system based on NDFT in contrast to those based on conventional discrete Fourier transform (DFT) with and without interpolation. In vivo imaging on human finger from volunteer is conducted at A-scan rate of 29 kHz and reconstruction is done based on different methods. The comparing results confirm that reconstruction method based on NDFT indeed improves sensitivity especially at large depth while maintaining the coherence-function-limited depth resolution.

Adaptive spectral doppler estimation

Abstract: In this paper, 2 adaptive spectral estimation techniques are analyzed for spectral Doppler ultrasound. The purpose is to minimize the observation window needed to estimate the spectrogram to provide a better temporal resolution and gain more flexibility when designing the data acquisition sequence. The methods can also provide better quality of the estimated power spectral density (PSD) of the blood signal. Adaptive spectral estimation techniques are known to provide good spectral resolution and contrast even when the observation window is very short. The 2 adaptive techniques are tested and compared with the averaged periodogram (Welch's method). The blood power spectral capon (BPC) method is based on a standard minimum variance technique adapted to account for both averaging over slow-time and depth. The blood amplitude and phase estimation technique (BAPES) is based on finding a set of matched filters (one for each velocity component of interest) and filtering the blood process over slow-time and averaging over depth to find the PSD. The methods are tested using various experiments and simulations. First, controlled flow-rig experiments with steady laminar flow are carried out. Simulations in Field II for pulsating flow resembling the femoral artery are also analyzed. The simulations are followed by in vivo measurement on the common carotid artery. In all simulations and experiments it was concluded that the adaptive methods display superior performance for short observation windows compared with the averaged periodogram. Computational costs and implementation details are also discussed.

Correlation Matching Approach for Spectrum Sensing in Open Spectrum Communications

Abstract: In the new paradigm of open spectrum access, the envisioned radio agility calls for fast and accurate spectrum sensing, this challenges traditional spectral estimation. In this study, we propose three new procedures that are able to sense the known spectrum of the candidate or primary user, fulfilling the requirements of open spectrum scenarios. These procedures are developed by following the framework of correlation matching, changing the traditional single frequency scan to a spectral scan with a particular shape and generalizing filter-bank designs. The proposed techniques are called Candidate methods, because their goal is to react only when the candidate's spectral shape is present. First, Candidate-F is proposed as a spectral detection method, where this is based on minimizing the Frobenius distance between correlation matrices, and can be viewed as an extended version of the weighted overlapped spectrum averaging estimate. Next, Candidate-G is presented, which is a new procedure that is based on a geodesic distance, and that presents the lowest complexity. Lastly, a third procedure is studied, Candidate-M, which provides the most compliant performance with the demanding open spectrum scenario by generalizing the Capon-spectral estimator. By means of the analytical results, simulations of receiver operating characteristics, and estimation variance, this study shows the advantages of Candidate-M over the existing filter bank or cyclostationary detector methods.

Monotone spectral density estimation

Abstract: We propose two estimators of a monotone spectral density, that are based on the periodogram. These are the isotonic regression of the periodogram and the isotonic regression of the log-periodogram. We derive pointwise limit distribution results for the proposed estimators for short memory linear processes and long memory Gaussian processes and also that the estimators are rate optimal.

Hilbert-Huang Transform Analysis of Hydrological and Environmental Time Series

Abstract: Climatic and hydrologic time series often display periodicities, and thus Fourier spectral analysis sometimes is appropriate. However, time series that are nonstationary, and also perhaps nonlinear, are not well handled by standard Fourier spectral analysis. Methods to handle nonstationarity, such as moving-time-window Fourier spectral analysis, assume linearity and have known limitations regarding the combined frequency and time resolution. For example, if the time series is stationary, then it is well known that better frequency resolution can be achieved by observing a longer time series (more time points). However, if the time series is nonstationary, then shorter time windows are required to estimate the “local in time” spectrum, analogous to using short-memory moving averages that use only the recent past few values to forecast the next value (P. Bloomfield, Fourier Analysis of Time Series: An Introduction, 2nd ed., John Wiley, 2000) because the mean value is changing over time. Therefore, in nonstationary time series analysis, there is a tension between the competing goals of time and frequency resolution. This tension is the reason that N. Huang et al. (Proc. R. Soc. A, 454, 903–995, 1998) introduced the Hilbert-Huang Transform (HHT) as an alternative to moving-time-window Fourier spectral analysis (Bloomfield, 2000).

Compressive spectral estimation for nonstationary random processes

Abstract: We propose a “compressive” estimator of the Wigner-Ville spectrum (WVS) for time-frequency sparse, underspread, nonstationary random processes. A novel WVS estimator involving the signal's Gabor coefficients on an undersampled time-frequency grid is combined with a compressed sensing transformation in order to reduce the number of measurements required. The performance of the compressive WVS estimator is analyzed via a bound on the mean square error and through simulations. We also propose an efficient implementation using a special construction of the measurement matrix.

Piecewise Wiener estimation for reconstruction of spectral reflectance image by multipoint spectral measurements

Abstract: This study proposes a piecewise Wiener estimation method to reconstruct a spectral reflectance image from a three-band image by multipoint spectral information collected simultaneously with image acquisition. A three-band image is divided into several blocks and the spectral estimation is carried out using the Wiener estimation matrix assigned to each block. Each Wiener estimation matrix is constructed on the basis of spectral measurement data. The experimental results show that the proposed method reduces the average estimation error monotonically as the number of spectral measurements increases. In addition, the computational time of the piecewise Wiener estimation costs only severalfold of the computational time of the conventional single-matrix method.

Adaptive scan-on-receive based on spatial spectral estimation for high-resolution, wide-swath Synthetic Aperture Radar

Abstract: Intensive research is currently ongoing in the field of Smart Multi-Aperture Radar Techniques (SMART) for high-resolution wide-swath Synthetic Aperture Radar (SAR) imaging. This work investigates the possibility of applying direction of arrival estimation methods to spaceborne SMART SAR systems, that employ receive beam steering. In particular, a new algorithm based on the actual spatial distribution of the received signal power is proposed. The performance of the algorithm is evaluated by Monte Carlo simulations and compared with that of the conventional scan-on-receive approach, in different operational scenarios. The Cramer Rao Lower Bound is also reported as a benchmark on the performance.

Electroencephalographic analysis for the assessment of hepatic encephalopathy: comparison of non-parametric and parametric spectral estimation techniques.

Abstract: Summary Objective To compare electroencephalographic spectral analysis obtained by periodogram (calculated by means of Fast Fourier Transform) and autoregressive (AR) modelling for the assessment of hepatic encephalopathy. Methods The mean dominant frequency (MDF) and the relative power of delta, theta, alpha, and beta bands were computed by both techniques from the electroencephalograms (EEG) of 201 cirrhotics and were evaluated in the clinical and prognostic assessment of the patients. Results The values of all the five indexes computed by periodogram and AR modelling matched each other, but the latter provided stable values after the analysis of fewer epochs. Independently of the technique, the relative power of theta and alpha bands fitted the clinical data and had prognostic value. The relative power of beta and delta bands computed by AR modelling fitted more closely with clinical data fitted the clinical data more closely. Conclusions The electroencephalographic spectral indexes obtained by periodogram and AR modelling were found to be, on average, undistinguishable, but the latter appeared less sensitive to noise and provided a more reliable assessment of low-power bands.

Method for interpolating seismic data by anti-alias, anti-leakage fourier transform

Abstract: An estimated frequency-wavenumber spectrum is generated by applying a first Anti-leakage Fourier transform method to aliased frequency components in temporal-transformed seismic data and applying a second Anti-leakage Fourier transform method to unaliased frequency components in the temporal-transformed seismic data. The second Anti-leakage Fourier transform method applies an absolute frequency-wavenumber spectrum extrapolated from unaliased frequencies to aliased frequencies to weight frequency-wavenumber components of the aliased frequencies. An inverse temporal and spatial Fourier transform is applied to the estimated frequency-wavenumber spectrum, generating trace interpolation of the seismic data.

Discrete cosine harmonic wavelet transform and its application to signal compression and subband spectral estimation using modified group delay

Abstract: This paper proposes a new harmonic wavelet transform (HWT) based on discrete cosine transform (DCTHWT) and its application for signal or image compression and subband spectral estimation using modified group delay (MGD). Further, the existing DFTHWT has also been explored for image compression. The DCTHWT provides better quality decomposed decimated signals, which enable improved compression and MGD processing. For signal/image compression, compared to the HWT based on DFT (DFTHWT), the DCTHWT reduces the reconstruction error. Compared to DFTHWT for the speech signal considered for a compression factor of 0.62, the DCTWHT provides a 30% reduction in reconstruction error. For an image, the DCTHWT algorithm due to its real nature, is computationally simple and more accurate than the DFTHWT. Further compared to Cohen–Daubechies–Feauveau 9/7 biorthogonal symmetric wavelet, the DCTHWT, with its computational advantage, gives a better or comparable performance. For an image with 6.25% coefficients, the reconstructed image by DFTHWT is significantly inferior in appearance to that by DCTHWT which is reflected in the error index as its values are 3.0 and 2.65%, respectively. For spectral estimation, DCTHWT reduces the bias both in frequency (frequency resolution) and spectral magnitude. The reduction in magnitude bias in turn improves the signal detectability. In DCTHWT, the improvement in frequency resolution and the signal detectability is not only due to good quality DCT subband signals but also due to their stretching (decimation) in the wavelet transform. The MGD reduces the variance while preserving the frequency resolution achieved by DCT and decimation. In view of these, the new spectral estimator facilitates a significant improvement both in magnitude and frequency bias, variance and signal detection ability; compared to those of MGD processing of both DFT and DCT fullband and DFT subband signals.

Empirical Mode Decomposition Based Instantaneous Spectral Analysis and its Applications to Heterogeneous Petrophysical Model Construction

Abstract: Summary Spectral analysis is an important step in seismic data processing and interpretation. The frequency contents of seismic traces vary with time due to the fact that the earth is non-stationary medium. Methods were available to improve the temporal and spectral resolution, such as windowed Fourier transform, wavelet transform, S-transform and Matching Pursuit Decomposition, etc. This paper described the Hilbert-Huang Transform (HHT) based on Empirical Mode Decomposition (EMD) that was initially developed to analyze nonlinear and non-stationary water waves. The advantage of HHT-EMD is that it does not require presumed a set of functions as previous methods and allows projection of a non-stationary and non-linear signal onto a time-frequency plane using a set of adaptive Intrinsic Mode Functions (IMF) only determined from the signal itself. The comparisons with wavelet transform and S-transform were made before HHT-EMD was applied to decompose well logs into the wave number-depth (k-z) domain. The depth varying spectrum function was obtained and then used to simulate locally stationary heterogeneous petrophysical models.

Fourier transform based spatial outlier mining

Abstract: Outlier detection is an important problem in spatial analysis which involves finding a region of spatial locations with features significantly different from the rest of the population. In this paper, we used fast fourier transform to highlight the areas with high frequency change. The spatial points identified by the fourier transform are then reconfirmed with Z-value test and outlier regions are identified. We performed several experiments to highlight the accuracy and efficiency of the approach and compared it with some other existing approaches.

Method and apparatus for spectral cross coherence

Abstract: The present invention relates to a machine implemented method for spectral analysis that determines a measure of cross coherence between application of two spectral estimation filters to data; and identifies a spectral feature of the measure of cross coherence. One example embodiment of the present invention provides a complete statistical summary of the joint dependence of the Bartlett and Capon power spectral statistics, showing that the coupling is expressible via a 2×2 complex Wishart matrix, where the degree coupling is determined by a single measure of cross coherence defined herein. This measure of coherence leads to a new two-dimensional algorithm capable of yielding significantly better resolution than the Capon algorithm, often commensurate with but at times exceeding finite sample based MUSIC.

Coherent spectral estimation for a robust solution of the permutation problem

Abstract: In this paper, we propose a new method based on a coherent source spectral estimation for solving the permutation problem of frequency-domain blind source separation (BSS). It combines the robustness of the State Coherence Transform (SCT) to recursively estimate a smooth phase spectrum associated with each source and the precision of the inter-frequency correlation to solve for a correct permutation. Namely, the TDOAs estimated by the SCT are used to constrain the permutation correction process in order to force the resulting filters to be coherent across frequency. This intrinsic interconnection between the TDOA information and the spectral correlation makes the new approach robust even when the signal is short in duration and spatial aliasing is substantial. Experimental results show that the proposed method is able to drastically reduce the number of permutation errors for three sources recorded in a short time block using microphones with large spacing.

Distance information obtainment method in endoscope apparatus and endoscope apparatus

Abstract: Distance information between an observation-target and each pixel of an imaging device is obtained in an endoscope apparatus. The endoscope apparatus includes a scope unit having an illumination-light illuminating unit and an imaging device, and a spectral image processing unit that generates a spectral estimation image signal of a predetermined wavelength by performing spectral image processing on an image signal output from the imaging device. The illumination-light illuminating unit illuminates the observation-target with illumination-light, and the imaging device images the observation-target by receiving light reflected from the observation-target illuminated with the illumination-light. The spectral image processing unit generates the spectral estimation image signal of the predetermined wavelength greater than or equal to 650 nm, as a spectral estimation image signal for obtaining distance information. Distance information representing a distance between the observation-target and each of the pixels is obtained based on the spectral estimation image signal for obtaining distance information.