Local-to global point cloud registration is a challenging task due to the substantial differences between these two types of data, and the different techniques used to acquire them. Global clouds cover large-scale environments and are usually acquired aerially, e.g., 3D modeling of a city using Airborne Laser Scanning (ALS). In contrast, local clouds are often acquired from ground level and at a much smaller range, for example, using Terrestrial Laser Scanning (TLS). The differences are often manifested in point density distribution, occlusions nature, and measurement noise. As a result of these differences, existing point cloud registration approaches, such as keypoint-based registration, tend to fail. We improve upon a different approach, recently proposed, based on converting the global cloud into a viewpoint-based cloud dictionary. We propose a local-toglobal registration method where we replace the dictionary clouds with viewpoint descriptors, consisting of panoramic range-images. We then use an efficient dictionary search in the Discrete Fourier Transform (DFT) domain, using phase correlation, to rapidly find plausible transformations from the local to the global reference frame. We demonstrate our method's significant advantages over the previous cloud dictionary approach, in terms of computational efficiency and memory requirements. In addition, We show its superior registration performance in comparison to a state-ofthe- art, keypoint-based method (FPFH). For the evaluation, we use a challenging dataset of TLS local clouds and an ALS large-scale global cloud, in an urban environment.

In studies on artificial bandwidth extension (ABE), there is a lack of international coordination in subjective tests between multiple methods and languages. Here we present the design of absolute category rating listening tests evaluating 12 ABE variants of six approaches in multiple languages, namely in American English, Chinese, German, and Korean. Since the number of ABE variants caused a higher-than-recommended length of the listening test, ABE variants were distributed into two separate listening tests per language. The paper focuses on the listening test design, which aimed at merging the subjective scores of both tests and thus allows for a joint analysis of all ABE variants under test at once. A language-dependent analysis, evaluating ABE variants in the context of the underlying coded narrowband speech condition showed statistical significant improvement in English, German, and Korean for some ABE solutions.

Many voice conversion systems require parallel training sets of the source and target speakers. Non-parallel training is more complicated as it involves evaluation of source-target correspondence along with the conversion function itself. INCA is a recently proposed method for non-parallel training, based on iterative estimation of alignment and conversion function. The alignment is evaluated using a simple nearest-neighbor search, which often leads to phonetic miss-matched source-target pairs. We propose here a generalized approach, denoted as Temporal-Context INCA (TC-INCA), based on matching temporal context vectors. We formulate the training stage as a minimization problem of a joint cost, considering both context-based alignment and conversion function. We show that TC-INCA reduces the joint cost and prove its convergence. Experimental results indicate that TC-INCA significantly improves the alignment accuracy, compared to INCA. Moreover, subjective evaluations show that TC-INCA leads to improved quality of the synthesized output signals, when small training sets are used.

A concatenative text-to-speech (CTTS) synthesizer requires a large acoustic database for high-quality speech synthesis. This database consists of many acoustic leaves, each containing a number of short, compressed, speech segments. In this paper, we propose two algorithms for recompression of the acoustic database, by recompressing the data in each acoustic leaf, without compromising the perceptual quality of the obtained synthesized speech. This is achieved by exploiting the redundancy between speech frames and speech segments in the acoustic leaf. The first approach is based on a vector polynomial temporal decomposition. The second is based on 3-D shape-adaptive discrete cosine transform (DCT), followed by optimized quantization. In addition we propose a segment ordering algorithm in an attempt to improve overall performance. The developed algorithms are generic and may be applied to a variety of compression challenges. When applied to compressed spectral amplitude parameters of a specific IBM small footprint CTTS database, we obtain a recompression factor of 2 without any perceived degradation in the quality of the synthesized speech.

The goal of voice conversion is to transform a sentence said by one speaker, to sound as if another speaker had said it. The classical conversion based on a Gaussian Mixture Model and several other schemes suggested since, produce muffled sounding outputs, due to excessive smoothing of the spectral envelopes. To reduce the muffling effect, enhancement of the Global Variance (GV) of the spectral features was recently suggested. We propose a different approach for GV enhancement, based on the classical conversion formalized as a GV-constrained minimization. Listening tests show that an improvement in quality is achieved by the proposed approach.

Concatenative synthesis and statistical synthesis are the two main approaches to text-to-speech (TTS) synthesis. Concatenative TTS (CTTS) stores natural speech features segments, selected from a recorded speech database. Consequently, CTTS systems enable speech synthesis with natural quality. However, as the footprint of the stored data is reduced, desired segments are not always available in the stored data, and audible discontinuities may result. On the other hand, statistical TTS (STTS) systems, in spite of having a smaller footprint than CTTS, synthesize speech that is free of such discontinuities. Yet, in general, STTS produces lower quality speech than CTTS, in terms of naturalness, as it is often sounding muffled. The muffling effect is due to over-smoothing of model-generated speech features. In order to gain from the advantages of each of the two approaches, we propose in this work to combine CTTS and STTS into a hybrid TTS (HTTS) system. Each utterance representation in HTTS is constructed from natural segments and model generated segments in an interweaved fashion via a hybrid dynamic path algorithm. Reported listening tests demonstrate the validity of the proposed approach.

Performance of a distributed video coding system depends, to a large extent, on the accuracy of joint source and side information distribution modeling. In this work we first examine a family of stationary joint distribution models. As one of our findings, we propose to use the double-Gamma model as an alternative to the widely adopted Laplace model, due to its superior performance. In addition, we suggest a new spatially adaptive model, which enables to follow the spatially varying joint statistics of the source and side information. We present two methods, class-based and neighborhood-based, for estimation of the spatially varying model parameters. We then show how the obtained pixel domain model can be used in the transform domain to facilitate utilization of frame spatial redundancy. Integration of the proposed models into a distributed video coding system resulted in improved performance.

Closed-form expressions for a new set of 3D rotation invariants that are linear, quadratic, and angular combinations of implicit polynomial (IP) coefficients are developed. Based on these invariants, we propose a 3D object recognition method that outperforms recognition based on IP fitting after pose estimation, and the MPEG-7 SSD technique.

In statistical HMM-based text-to-speech systems (STTS), speech feature dynamics is modeled by first- and second-order feature frame differences, which, typically, do not satisfactorily represent frame to frame feature dynamics present in natural speech. The reduced dynamics results in over-smoothing of speech features, often sounding as muffled synthesized speech. In this correspondence, we propose a method to enhance a baseline STTS system by introducing a segment-wise model representation with a norm constraint. The segment-wise representation provides additional degrees of freedom in speech feature determination. We exploit these degrees of freedom for increasing the speech feature vector norm to match a norm constraint. As a result, statistically generated speech features are less over-smoothed, resulting in more natural sounding speech, as judged by listening tests.

This paper presents a model-based transrating (bit-rate reduction) system for H.264 coded video via requantization. In works related to previous standards, optimal requantization step sizes were obtained via Lagrangian optimization that minimizes the distortion subject to a rate constraint. Due to H.264 advanced coding features, the choices of quantization step size and coding modes are dependent and the rate control becomes computationally expensive. Therefore, optimal requantization algorithms developed for previous standards cannot be applied as they are. Hence, previous works on transrating in H.264 focused on changing the input coding decisions rather than on rate control, while requantization was addressed by a simple one-pass algorithm. Here we propose new model-based optimal requantization algorithms for transrating of H.264 coded video. The optimal requantization goal is to achieve the target bit rate with minimal effect on video quality. Incorporation of the proposed models serves two goals. For intra-coded frames, a novel closed-loop statistical estimator that overcomes spatial neighbors dependencies is developed. For inter-coded frames, the proposed macroblock-level models reduce the computational burden of the optimization. Overall, as compared to re-encoding (cascaded decoder-encoder), the proposed system reduces the computational complexity by a factor of about four, at an average PSNR loss of only 0.4 dB for transrating CIF/SIF sequences from 2 Mb/s to 1 Mb/s. In comparison with a simple one-pass requantization, the proposed algorithm achieves better performance (an average PSNR gain of 0.45 dB), at the cost of just twice the complexity.

In statistical TTS systems (STTS), speech features dynamics is modeled by firstand second-order feature frame differences, which, typically, do not satisfactorily represent frame to frame feature dynamics present in natural speech. The reduced dynamics results in over smoothing of speech features, often sounding as muffled synthesized speech. To improve feature dynamics a Global Variance approach has been suggested. However, it is computationally complex. We propose a different approach for modeling feature dynamics based on applying the DFT to the whole set of feature frames representing a phoneme. In the transform domain the inter-frame feature dynamics is then expressed in terms of inter-harmonic content, which can be modified to statistically match the dynamics of natural speech. To synthesize a whole utterance we propose a method for smoothly combining the enhanced-dynamics phonemes, which improves synthesized speech quality of STTS with similar complexity to conventional STTS.

Show-Through is a common occurrence when scanning duplex printed documents. The back-side printing shows through the paper, contaminating the front side image. Previous work modeled the problem as anon-linearconvolutivemixtureof images and offered solutions based on decorrelation. In this work we propose a cleaning process based on a Blind Source Separation approach. We define a cost function incorporating the non-linear mixing model in a mean-squared error term, along with a regularization term based on Total-Variation. We propose a location dependent regularization tradeoff, preserving image edges while removing show-through edges. The images and mixing parameters are estimated using an alternating minimization process, with each stage using only convex optimization methods. The resulting images exhibit significantly lower show-through, both visibly and in objective measures.

Audio streaming applications have become very popular in recent years, owing to their low cost and convenience. However, during network congestions, data packets are often delayed or discarded, creating an annoying gap in the streamed media. This letter presents a new approach to audio packet loss concealment designed for MPEG-audio streaming applications. In a previous work, we introduced a receiver-based concealment algorithm based on applying the gapped-data amplitude and phase estimation (GAPES) interpolation algorithm in the discrete short-time Fourier transform (DSTFT) complex domain and obtained better results compared to past methods. The current approach applies the same algorithm on a different complex domain, formed from combining the modified discrete cosine transform (MDCT) domain as its real part and the modified discrete sine transform (MDST) domain as the imaginary part. The new approach significantly reduces the complexity demands while maintaining similar high-quality results.

Vector quantization (VQ) is an effective and widely known method for low-bit-rate communication of speech and image signals. A common assumption in the design of VQ-based communication systems is that the compressed digital information is transmitted through a perfect channel. Under this assumption, quantization distortion is the only factor in output signal fidelity. Moreover, the assignment of channel symbols to the VQ reconstruction vectors is of no importance. However, under physical channels, errors may be present, causing degradation in overall system performance. In such a case, the effect of channel errors on the coding system performance depends on the index assignment of the reconstruction vectors. The index assignment problem is a special case of the Quadratic Assignment Problem (QAP) and is known to be NP-complete. For a VQ with N reconstruction vectors there are N! possible assignments, meaning that an exhaustive search over all possible assignments is practically impossible. To help the VQ designer, we present in this correspondence lower and upper bounds on the performance of VQ systems under channel errors, over all possible assignments. The bounds coincide with a general bound for the QAP. Nevertheless, the proposed derivation allows us to compare the bounds with published results on VQ index assignment. A related expression for the average performance is also given and discussed. Special cases and numerical examples are given in which the bounds and average performance are compared with index assignments obtained by known algorithms.

The Optimized Temporal Decomposition (OTD) technique for Line Spectral Frequencies (LSF) speech envelope representation, under a MMSE criterion, has been shown to be promising for very low bit rate speech coding for storage and broadcast applications. In order to improve perceptual speech quality, a dynamically weighted OTD (DW-OTD) technique is introduced in this work. It extends the OTD by allowing temporally changing weights, so as to improve the perceived speech quality. Use of Gardner's weighted MSE with DWOTD is found to reduce the Log Spectral Distance (LSD) measure by 0.3 dB, as compared to OTD. The original OTD algorithm delay and complexity requirements make it inappropriate for real-time speech coding. In this paper we also introduce a modification of this technique, which is suboptimal but suitable for on-line speech coding purposes, with negligible degradation of performance (of only about 0.06 dB in LSD). With the proposed techniques we were able to encode speech spectral envelopes at 300-370 bps at LSD of 2.25-2.1 dB, respectively, with a delay of just 7 frames.

This work deals with fitting 2D and 3D implicit polynomials (IPs) to 2D curves and 3D surfaces, respectively. The zero-set of the polynomial is determined by the IP coefficients and describes the data. The polynomial fitting algorithms proposed in this paper aim at reducing the sensitivity of the polynomial to coefficient errors. Errors in coefficient values may be the result of numerical calculations, when solving the fitting problem or due to coefficient quantization. It is demonstrated that the effect of reducing this sensitivity also improves the fitting tightness and stability of the proposed two algorithms in fitting noisy data, as compared to existing algorithms like the well-known 3L and gradient-one algorithms. The development of the proposed algorithms is based on an analysis of the sensitivity of the zero-set to small coefficient changes and on minimizing a bound on the maximal error for one algorithm and minimizing the error variance for the second. Simulation results show that the proposed algorithms provide a significant reduction in fitting errors, particularly when fitting noisy data of complex shapes with high order polynomials, as compared to the performance obtained by the abovementioned existing algorithms.

Vector quantization (VQ) is a widely implemented method for low-bit-rate signal coding. A common assumption in the design of VQ systems is that the digital information is transmitted through a perfect channel. Under this assumption, the assignment of channel symbols to the VQ reconstruction vectors (RV) is of no importance. However, under physical channels, the effect of channel errors on the VQ system performance depends on the index assignment of the RV. For a VQ of size N, there are N! possible assignments, meaning that an exhaustive search over all possible assignments is practically impossible. Lower and upper bounds on the performance of VQ systems under channel errors, over all possible assignments, are presented. A related expression for the average performance is also discussed. Numerical examples are given in which the bounds and average performance are compared with index assignments obtained by the index-switching algorithm.

A color-mapped image is composed of a palette and an image of indices. The color of each pixel is the palette color referenced by the pixel's index. The indices image is usually far from being smooth. This affects its compressibility by lossless image compression methods, due to their reliance on the natural smoothness of images. The indices images can be smoothed by re-ordering the palette, ie, assigning the indices to the colors in a better way. We propose a new method for palette ordering based on an approximate solution of the traveling salesman problem. The proposed method has low complexity, dependent only on the size of the palette. It is found to significantly improve the compression ratios of color-mapped images coded with the JPEG-LS lossless compression standard. The resulting compression ratios are better than those achieved by the popular GIF image file format.

Fractal image coding is a relatively new technique for compact image representation. The basic coding scheme exploits self-similarities between parts of the image and other parts in it at a different resolution. The various parts are consequences of a partition grid obtained by applying a splitting criterion to the image. We present an algorithm for adaptive image partitioning, achieving designated rates under a computational complexity constraint. The proposed algorithm results in a reduction of the computational complexity as compared to other known algorithms at the same rate-distortion operating point. Also presented is an efficient procedure for approximating parts in the image by a linear combination of two other parts in it and its combination with the adaptive partitioning algorithm.

This work considers the efficient coding of hyperspectral images. The shape-adaptive DCT is extended to the three-dimensional case. Both the 3D-SA-DCT and the conventional 3D-DCT are combined with either of two alternative techniques for coding the transform coefficients. The proposed schemes are compared with two state of the art coding algorithms, which serve as benchmarks, and are found to have better performance (in terms of PSNR). Particularly good performance is shown by the coder based on the 3D-SA-DCT in a classification task applied to the coded images.

Presents an approach to contour representation and coding. It consists of an improved fitting of high-degree (4/sup th/ to 18/sup th/) implicit polynomials (IPs) to the contour which is robust to coefficient quantization. The proposed approach to solve the fitting problem is a modification of the 3L linear solution developed by Lei et al. (1997) and is more robust to noise and to coefficient quantization. We use an analytic approach to limit the maximal fitting error between each data point and the zero-set generated by the quantized polynomial coefficients. We than show that consideration of the quantization error (which led to a specific sensitivity criterion) also brought about a significant improvement in fitting IPs to noisy data, as compared to the 3L algorithm.

Speech enhancement algorithms which are based on estimating the short-time spectral amplitude of the clean speech have better performance when a soft-decision gain modification, depending on the a priori probability of speech absence, is used. In reported works a fixed probability, q, is assumed. Since speech is non-stationary and may not be present in every frequency bin when voiced, we propose a method for estimating distinct values of q for different bins which are tracked in time. The estimation is based on a decision-theoretic approach for setting a threshold in each bin followed by short-time averaging. The estimated q's are used to control both the gain and the update of the estimated noise spectrum during speech presence in a modified MMSE log-spectral amplitude estimator. Subjective tests resulted in higher scores than for the IS-127 standard enhancement algorithm, when pre-processing noisy speech for a coding application.

This paper presents new properties of the discrete morphological skeleton representation of binary images, along with a novel coding scheme for lossless binary image compression that is based on these properties. Following a short review of the theoretical background, two sets of new properties of the discrete morphological skeleton representation of binary images are proved. The first one leads to the conclusion that only the radii of skeleton points belonging to a subset of the ultimate erosions are needed for perfect reconstruction. This corresponds to a lossless sampling of the quench function. The second set of new properties is related to deterministic prediction of skeletonal information in a progressive transmission scheme. Based on the new properties, a novel coding scheme for binary images is presented. The proposed scheme is suitable for progressive transmission and fast implementation. Computer simulations, also presented, show that the proposed coding scheme substantially improves the results obtained by previous skeleton-based coders, and performs better than classical coders, including run-length/Huffman, quadtree, and chain coders. For facsimile images, its performance can be placed between the modified read (MR) method (K=4) and modified modified read (MMR) method.

The Wigner distribution (WD) possesses a number of desirable mathematical properties relevant to time-frequency analysis. However, the presence of interference terms renders the WD of multicomponent signals extremely difficult to interpret. We propose an adaptive decomposition of the WD using extended libraries of orthonormal bases. A prescribed signal is expanded on a basis of adapted waveforms, that best match the signal components, and subsequently transformed into the Wigner domain. The interference terms are controlled by thresholding the cross WD of interactive basis functions according to their degree of adjacency in an idealized time-frequency plane. This measure is implicitly adapted to the local distribution of the signal, thus compensating for a global nonadaptive threshold. In particular we focus on a shift-invariant decomposition in an extended library of wavelet packets. The resulting modified distribution achieves high time-frequency resolution, and is superior in eliminating interference terms associated with bilinear distributions.

A shifted wavelet packet (SWP) library, containing all the time shifted wavelet packet bases, is defined. A corresponding shift-invariant wavelet packet decomposition (SIWPD) search algorithm for a "best basis" is introduced. The search algorithm is representable by a binary tree, in which a node symbolizes an appropriate subspace of the original signal. We prove that the resultant "best basis" is orthonormal and the associated expansion, characterized by the lowest "information cost", is shift-invariant. The shift-invariance stems from an additional degree of freedom, generated at the decomposition stage, and incorporated into the search algorithm. We prove that for any subspace it suffices to consider one of two alternative decompositions, made feasible by the SWP library. The computational complexity of SIWPD may be controlled at the expense of the attained information cost, to an extent of O(2Nlog/sub 2/N).

A general formulation of shift-invariant "best-basis" expansions is presented. Specifically, we construct an extended library of smooth local trigonometric bases, and introduce a suitable "best-basis" search algorithm. We prove that the resultant decomposition is shift-invariant, orthonormal and characterized by a reduced information cost. The shift-invariance is derived from an adaptive relative shift of expansions in distinct resolution levels. We show that at any resolution level it suffices to examine and select one of two relative shift options a zero shift or a 2-e-1 shift. A variable folding operator, whose polarity is locally adapted to the parity properties of the signal, extra enhances the representation.

Dynamic time warping (DTW) is a dynamic programming technique widely used for solving time-alignment problems. The classical DTW constrains only the first derivative of the warping function, hence allowing no direct control over the warping function curvature. Moreover, it implicitly assumes-inappropriately for some applications-that the noise is white. We propose a multidimensional dynamic-programming technique which can efficiently solve time-warping optimization problems involving colored noise, and allows control over the warping function curvature. The technique is demonstrated for the co-channel speech separation problem. Applications employing DTW can benefit from the new technique, which offers improved accuracy and robustness in the presence of colored noise and competing speech.

A transform trellis coding scheme is used to encode images at rates below 1 b/pel, resulting in high SNR values and very good subjective performance. A discrete cosine transform (DCT) is used to decorrelate the data samples, and a trellis encoder is used to encode the transformed coefficients. To overcome image nonstationarity, a clustering algorithm is used to segment the transformed image into distinct regions and a separate trellis diagram is then constructed for each cluster. To further improve the image quality, particularly to reduce the blocking effect, a scalar quantizer is used to quantize large-magnitude coefficients of the error image in the transform domain. The performance of the proposed scheme is found to be better than that of other recently reported schemes.>

A maximum a posteriori approach for enhancing speech signals which have been degraded by statistically independent additive noise is proposed. The approach is based upon statistical modeling of the clean speech signal and the noise process using long training sequences from the two processes. Hidden Markov models (HMMs) with mixtures of Gaussian autoregressive (AR) output probability distributions are used to model the clean speech signal. A low-order Gaussian AR model is used for the wideband Gaussian noise considered here. The parameter set of the HMM is estimated using the Baum or the EM (estimation-maximization) algorithm. The enhancement of the noisy speech is done by means of reestimation of the clean speech waveform using the EM algorithm. An approximate improvement of 4.0-6.0 dB in signal-to-noise ratio (SNR) is achieved at 10 dB input SNR.>

The efficient representation of the excitation signal to an LPC (linear predictive coding) synthesis filter by means of a vector expansion of the residual signal is examined. According to this approach the excitation signal is represented as a linear combination of a small number of vectors taken from a given vector set, known at both ends of the transmission channel. It is demonstrated that this approach provides a unified framework for describing and analyzing a wide range of residual speech coders, from multipulse LPC and code-excited linear prediction to residual transform coders, and leads to generalization of some of these schemes. Optimally conditions based on the singular-value decomposition (SVD) of the impulse-response matrix of the perceptually weighted LPC synthesis filter are given. A resulting simplified predictive transform coder is proposed and examined by computer simulation.>

An algorithm is proposed for enhancing noisy speech which has been degraded by statistically independent additive noise. The algorithm is based on modeling the clean speech as a hidden Markov process with mixtures of Gaussian autoregressive (AR) output processes and modeling the noise as a sequence of stationary, statistically independent, Gaussian AR vectors. The parameter sets of the models are estimated using training sequences from the clean speech and the noise process. The parameter set of the hidden Markov model is estimated by the segmental k-means algorithm. Given the estimated models, the enhancement of the noisy speech is done by alternate maximization of the likelihood function of the noisy speech, one over all sequences of states and mixture components assuming that the clean speech signal is given, and then over all vectors of the original speech using the resulting most probable sequence of states and mixture components. This alternating maximization is equivalent to first estimating the most probable sequence of AR models for the speech signal using the Viterbi algorithm, and then applying these AR models for constructing a sequence of Wiener filters which are used to enhance the noisy speech.>

A statistical model is used for the optimal design of analysis/synthesis systems which include quantization of the signals in the separate bands. Two error measures are used. One is a generalization of the usual statistical mean square error (MSE) to time-varying systems (since analysis/synthesis systems with decimation and interpolation are time varying). The second measure is the time average of the expected ℓ2distance between the output of the analysis stage and the analyzed reconstructed signal. The proposed design methods are based on minimizing these error measures and shown to apply not only with the DFT but also with any linear regular transform (e.g. Hadamard, DCT). The above two error measures are shown to be equivalent for a wide class of transforms (including the DFT). The design methods is applicable to either finding an optimal synthesis window for a given analysis window, or finding an optimal analysis window for a given synthesis window. The optimal windows (filters) are obtained by solving a set of linear equations. An optimal analysis/synthesis system is obtained using an iterative algorithm which is based on alternately solving these two sets of linear equations. When no quantization is applied the new design methods coincides with previously reported methods.

Two new ideas for the design of multipulse excited LPC coders are presented in this paper. The first idea relates to improving the extraction of the all-pole filter parameters by taking into account the error weighting function. This function, which takes advantage of the noise masking properties of the ear, is ignored in the conventional covariance and autocorrelation methods. The new approach leads to an iterative algorithm, in which the first iteration is essentially the covariance method. Each new iteration involves estimation of the residual and increases a likelihood function, taking into account the error weighting function. The second idea relates to improving the derivation of the excitation parameters. A recursive algorithm for the optimal choice of the i-th pulse location, given the previously obtained (i-l) pulse locations, is presented. The low complexity of the new algorithm enables its combination with a tree search algorithm, thus giving a solution which is better than the solution of the algorithms reported earlier , when the error weighting function is used

In this paper we present a method for computing the synthesis filter (window) needed in a weighted overlap-add (WOLA) scheme for the reconstruction of signals in analysis/synthesis systems used to implement the discrete short time Fourier transform (DSTFT). The method is based on an algebraic representation of the analysis synthesis process and assume that the analysis filter (window) is known, that its length N is larger than or equal to the transform size (i.e. the number of frequency bands) M, that no modification of the DSTFT is performed, and that exact signal reconstruction (unity system) is to be achieved. The last condition can be achieved only if the shift R of the sliding analysis window satisfiesl \leq R < M. The solution presented in this paper extends an earlier result obtained for N = M, and is of practical importance, since using N > M results in analysis filter banks with improved frequency band separation. The algebraic method presented is simple and efficient as it reduces the large dimensionality of the problem into a solution of R sets of linear equations of reduced dimensions. The solutions of these equations are the individual synthesis polyphase filters from which the synthesis filter is constructed.

An algebraic representation of the discrete short-time Fourier transform (DSTFT) is presented for the case in which the analysis window lengthNequals the transform block sizeM. This representation allows the application of algebraic tools for determining an optimal synthesis system which minimizes the mean square error between a given modified DSTFT (which is not necessarily a valid DSTFT sequence) and the DSTFT of the synthesized signal. If no modification is applied, the result is a unity analysis-synthesis system for any given time updateRof the sliding analysis window (provided thatR\leqM). It is shown that the optimal synthesis system can be implemented by the well known weighted overlap-add (WOLA) method using an optimal synthesis window. The algebraic approach enables the extension of some recent results and the relaxation of a constraint on the analysis window. The proposed approach is found also to have a potential for solving the synthesis problem for the more general case ofN>M.

A speech enhancement system which utilizes an optimal (in the minimum mean square error sense) short-time spectral amplitude estimator is described. The derivation of the optimal estimator is based on modeling speech as a quasi-periodic signal, and on applying spectral decomposition. The optimal spectral amplitude estimator and a recently developed vector spectral subtraction amplitude estimator, are round to be nearly equivalent. The optimal spectral amplitude estimator coincides with a Wiener spectral amplitude estimator at high signal to noise ratio (SNR) values, and is found to be superior to it at low SNR values. The enhanced speech obtained by using the proposed system, is less spectrally distorted, although contains some more residual noise, than the enhanced speech obtained by using the Wiener spectral amplitude estimator, in the same system. In addition, it is free of the "musical noise" characteristic to the spectral subtraction algorithm. Both systems, the proposed one and spectral subtraction, have approximately the same complexity.

The cepstral representation of the LPC residual signal was found in an earlier study to provide a convenient framework for efficiently representing the main spectral components of the residual signal. The analysis-synthesis system developed uses in general poles, zeros and cepstral residual terms to better represent the input speech signal spectrum and achieves improved synthesized speech quality. In this work we consider the quantization of the system parameters and its effect on the system performance in terms of spectral mismatch. The results are used to develop a cepstral residual vocoder system for 4.8 kbps transmission of speech. The system is based on an 8-pole LPC model and the cepstral representation of the residual signal. It applies optimal uniform quantization to each term of the cepstral residual and obtains most of the attainable spectral matching at this useful rate. The study is based on computer simulations with telephone bandwidth speech.

Because of speech signals nonstationarity, usual comb filtering of noisy speech signals results only in a modest improvement in signal to noise ratio, and only in a small perceptual reduction of structured noise or interference. A generalized comb filtering technique, which applies a time-varying weighting to each pitch period, is mathematically analyzed and shown to be capable of breaking up the noise structure, in addition to comb filtering. This is found to provide a meaningful perceptual improvement when the noise or interference are structured. The mathematical analysis is facilitated by using a polyphase network model of the generalized comb filter. Design constraints and rules are developed and several filter families are proposed. Computer simulation results are discussed.

Periodically structured noise is noise which occurs randomly but with a fixed or slowly varying period. The noise periodicity is usually due to some underlying process, such as block processing of the speech where discontinuities between successive blocks result. This type of noise permeates the entire speech spectrum and is not removable by standard filtering techniques. The recently developed time domain harmonic sealing (TDHS) algorithm has been found to be the basis for an effective enhancement technique. In this paper we discuss the underlying theory of this technique and establish a class of windows for its implementation. As an example the frame rate noise of adaptive transform coding was perceptually reduced using this technique. Results from a subjective testing experiment using ATC coded speech with bit rates of 7.2 to 16 Kb/s indicated an improvement in quality equivalent to an increase in code rate of 2.4 to 3 Kb/s for speech originally coded at 7.2 to 12 Kb/s.

A basic reason for the loss of quality in LPC synthesized speech is the spectral mismatch between the LPC model and the analyzed speech. Since the residual signal contains the spectral information not extracted by the LPC analyzer, existing methods for improving the synthesized speech quality are based on encoding the residual signal or reducing its information content by improved pole-zero models. In this work we study an approach in which the spectral envelope of the residual signal is divided into three main components which are separately represented to provide an overall more efficient representation of the residual signal. The three spectral components are the antiresonances due to nasalized sounds, fixed or slowly varying spectral contributions due to input spectral shaping, and the remaining spectral matching error. An analysis-synthesis system based on the cepstral representation of the residual signal is developed and is shown to provide a particularly convenient framework for separating and representing by zeroes and cepstral residuals the different spectral components. Preliminary results of the study are presented on the basis of simulations with telephone bandwidth speech.

In this presentation we report on preliminary results obtained with a speech encoding scheme which combines time-domain harmonic compression (TDHC) and CVSD for digital transmission of speech signals at 7.2 kbit/s. The harmonic compression at the transmitter and the harmonic expansion at the receiver are performed with the recently developed time-domain harmonic scaling (TDHS) algorithms. By using a frequency-scaling factor of 2 and the encoding of the frequency-divided signal with a 7.2 kbit/s CVSD coder, the reconstructed speech has a quality close to that achieved by a 14.4 kbit/s plain CVSD. The complexity of the proposed system is significantly lower than more elaborate waveform coders or vocoders which can operate at 7.2 kbit/s. Issues involved in the proposed combination are discussed and computer simulation results are presented.

In this presentation the stable and optimal operation of the adaptive line enhancer (ALE) is considered. By improving the estimate of the steady-state mean square error (MSE), a tighter stability constraint is obtained, as well as more accurate expressions for the SNR gain attained by the ALE when filtering sinusoidal signals in white noise. Since the LMS algorithm used for adapting the ALE weights aims at minimizing the MSE, and not at maximizing the output SNR, the proper choice of the algorithm's parameters for maximizing the SNR gain is considered. In particular, it is shown that for a given step-size parameter µ (which satisfies the stability constraint) there exists an optimal number of weights which maximizes the SNR gain. Computer simulations verify the analytical results.