waashk/dblp huggingface dataset:
optimal and robust waveform design for mimo radars. waveform design for target identification and classification in mimo radar systems has been studied in several recent works. while the previous works considered signal independent noise, here we extend the results to the case where signal-dependent noise, clutter, is also present and then we find the optimum waveform for several estimators differing in the assumptions on the given statistics. computing the optimal waveforms for mmse estimator leads to the semi-definite programming (sdp) problem. finding the optimal transmit signals for csls estimator results in a minimax eigenvalue problem. finally it is shown that equal power waveforms are the best transmit signals for the sls estimator.
multi-user mimo and adaptive frequency reuse for next-generation mobile broadband networks. in order to meet the constantly increasing demand for ubiquitous, mobile access to the internet, next-generation mobile broadband communications systems based on ofdma, such as ieee 802.16m, require a significant performance increase over previous generation systems, such as ieee 802.16e-2005, particularly in cell-edge and average spectral efficiency [2]. in this paper, we address the downlink adaptive frequency reuse (afr) and multi-user mimo (mu-mimo) techniques which are considered to be the most promising candidates for meeting the requirements on cell-edge and average spectral efficiency of next-generation mobile broadband systems.
state-space analysis on time-varying correlations in parallel spike sequences. a state-space method for simultaneously estimating time-dependent rate and higher-order correlation underlying parallel spike sequences is proposed. discretized parallel spike sequences are modeled by a conditionally independent multivariate bernoulli process using a log-linear link function, which contains a state of higher-order interaction factors. a nonlinear recursive filtering formula is derived from a log-quadratic approximation to the posterior distribution of the state. together with a fixed-interval smoothing algorithm, time-dependent log-linear parameters are estimated. the smoothed estimates are optimized via em-algorithm such that their prior covariance matrix maximizes the expected complete data log-likelihood. in addition, we perform model selection on the hierarchical log-linear state-space models to avoid over-fitting. application of the method to simultaneously recorded neuronal spike sequences is expected to contribute to uncover dynamic cooperative activities of neurons in relation to behavior.
plane-wave decomposition of a sound scene using a cylindrical microphone array. the analysis for microphone arrays formed by mounting microphones on a sound-hard spherical or cylindrical baffle is typically performed using a decomposition of the sound field in terms of orthogonal basis functions. an alternative representation in terms of plane waves and a method for obtaining the coefficients of such a representation directly from measurements was proposed recently for the case of a spherical array. it was shown that representing the field as a collection of plane waves arriving from various directions simplifies both source localization and beamforming. in this paper, these results are extended to the case of the cylindrical array. similarly to the spherical array case, localization and beamforming based on plane-wave decomposition perform as well as the traditional orthogonal function based methods while being numerically more stable. both simulated and experimental results are presented.
emg signal denoising via bayesian wavelet shrinkage based on garch modeling. in this paper, we introduce a novel noise suppression method for electromyography (emg) signals, based on statistical modeling of wavelet coefficients. first, we demonstrate that generalized autoregressive conditional heteroscedasticity (garch) effect exists in wavelet coefficients of emg signals. then, we use garch model for these coefficients. in consequence, we introduce a maximum a-posteriori (map) estimator, based on garch modeling, for estimating the clean wavelet coefficients. to evaluate the performance of garch based method in noise suppression, we compare our proposed method with other wavelet based denoising methods and we verify the performance improvement in utilizing the new strategy.
spatial redundancy in higher order ambisonics and its use for lowdelay lossless compression. when higher order ambisonics (hoa) is used to represent a sound field, the channels might contain a lot of redundancy in some cases. this redundancy can be exploited in order to provide more efficient network transmission and storage. in this work the amount of inter-channel redundancy for higher order ambisonics is investigated. furthermore, lossless compression techniques that build on this redundancy are studied, with a focus on low-delay algorithms for real-time, or two-way, applications. the presented encoding scheme results in a delay of 256 samples, but with a rather high computational complexity both for encoding and decoding. the system also preserves the desired features of the hoa format, such as the scalability and the ability to reproduce over arbitrary loudspeaker layouts.
high resolution audio synchronization using chroma onset features. the general goal of music synchronization is to automatically align the multiple information sources such as audio recordings, midi files, or digitized sheet music related to a given musical work. in computing such alignments, one typically has to face a delicate tradeoff between robustness and accuracy. in this paper, we introduce novel audio features that combine the high temporal accuracy of onset features with the robustness of chroma features. we show how previous synchronization methods can be extended to make use of these new features. we report on experiments based on polyphonic western music demonstrating the improvements of our proposed synchronization framework.
the sri nist 2008 speaker recognition evaluation system. the sri speaker recognition system for the 2008 nist speaker recognition evaluation (sre) incorporates a variety of models and features, both cepstral and stylistic. we highlight the improvements made to specific subsystems and analyze the performance of various subsystem combinations in different data conditions. we show the importance of language and nativeness conditioning, as well as the role of asr for speaker verification.
optimal parameter estimation for model-based quantization. we address optimal model estimation for model-based vector quantization for both the constrained resolution (cr) and constrained entropy (ce) cases. to this purpose we derive under high-rate (hr) theory assumptions the rate-distortion (rd) relations for these two quantization scenarios assuming a gaussian model. based on the rd relations we show that the maximum likelihood (ml) criterion leads to optimal performance for ce quantization, but not for cr quantization. we introduce a new model estimation criterion for cr quantization that is optimal (under hr theory assumptions) in terms of the rd relation. our experiments confirm that the proposed criterion for model identification outperforms the ml criterion for a range of conditions.
blind ranges, frequencies and doas estimation for near field sources. two novel algorithms are proposed for ranges, frequencies and direction-of-arrivals (doas) estimation of the narrow-band near field sources. by exploiting the time-domain and space-domain correlations of impinging signals jointly, we can identify 2p independent sources and estimate 3 groups of parameters with a uniformly linear array (ula) of 2p + 1 elements, being double of the conventional methods. in addition, the peak search and pairing operations, needed in most algorithms for near field sources, can be omitted completely in our methods. simulation results show that our two esprit-based methods provide the improved performance over conventional ones based on the second-order statistics.
sparse decomposition of mixed audio signals by basis pursuit with autoregressive models. we develop a framework to detect when certain sounds are present in a mixed audio signal. we focus on the regime where out of a large number of possible sounds, a small but unknown number are combined and overlapped to yield the observed signal. to infer which sounds are present, we attempt to decompose the observed signal as a linear combination of a small number of sources. to encourage sparse solutions with this property, we balance the modeling errors from individual sources against an ℓ1-norm penalty of the type used in basis pursuit and regularized linear regression with grouped variables. our approach can be viewed as a novel generalization of basis pursuit in two ways: first, with a dictionary of fixed size, we attempt to model acoustic waveforms of potentially variable duration; second, for dictionary entries, we do not store basis vectors representing static templates, but the coefficients of autoregressive models that characterize the acoustic variability of individual sources. we derive the required optimizations in this framework and present experimental results on combinations of periodic and aperiodic sources.
graphical models: statistical inference vs. determination. using discrete hidden-markov-models (hmms) for recognition requires the quantization of the continuous feature vectors. in handwritten whiteboard note recognition it turns out that the pen-pressure information, which is important for recognition, is not adequately quantized and looses significance. in this paper, the implicit modeling of the pressure information presented in previous work which uses the deterministic knowledge on the actual pressure is generalized using a graphical model (gm) representation based on statistical inference. the results of two state-of-the-art toolboxes implementing hmms and gms are compared. it can be seen that the statistical inference approach based on gms is inferior to the implicit modeling of the pressure information. it is shown that a direct implementation of hmms outperforms the mathematic identical gm representation.
acoustic compensation methods for body transmitted speech conversion. statistical voice conversion is very effective for enhancing body transmitted speech recorded with non-audible murmur (nam) microphone. in this method, a probabilistic model to convert body transmitted speech into natural speech is trained previously. because acoustic characteristics of body transmitted speech is sensitive to recording conditions such as a location of nam microphone, significant degradation of the conversion performance is often caused in practical situations by acoustic mismatches between training and conversion processes. to alleviate this problem, we propose unsupervised acoustic compensation methods for body transmitted voice conversion. experimental results demonstrate that the proposed methods significantly reduce the quality degradation of converted speech caused by the acoustic mismatches.
on a tradeoff between dereverberation and noise reduction using the mvdr beamformer. the minimum variance distortionless response (mvdr) beamformer can be used for both speech dereverberation and noise reduction. in this paper we analyse the tradeoff between the amount of speech dereverberation and noise reduction achieved by the mvdr beamformer. we show that the amount of noise reduction that is sacrificed when desiring both speech dereverberation and noise reduction depends on the direct-to-reverberation ratio of the acoustic transfer function between the desired source and a reference microphone. the performance evaluation supports the theoretical analysis and demonstrates the tradeoff between speech dereverberation and noise reduction.
optimized opportunistic multicast scheduling (oms) over heterogeneous cellular networks. optimized opportunistic multicast scheduling (oms) has been studied previously by the authors for homogeneous cellular networks, where the problem of efficiently transmitting a common set of data from a single base station to multiple users that have identical channel statistics was examined. it has been demonstrated that oms can achieve significant performance improvement by exploiting the optimal tradeoff between multiuser diversity and multicast gain. in this work, we extend our studies to heterogeneous networks with users subject to different channel statistics. specifically, we consider a single cell wireless network with users uniformly distributed in a circular region around the base station. since users with low snr are the ones that hinder system throughput, we argue that system performance may be predicted by the behavior of users in the outmost ring of the cell, which are approximately homogeneous. using extreme value theory and results obtained from the homogeneous case, we determine the optimal user selection ratio for a homogeneous ring of users near the edge of the cell and then use it to derive the optimal selection ratio over the entire heterogeneous network. simulations confirm theoretical results and illustrate the effectiveness of the proposed scheme.
hands-on engineering and science: discovering cosmic rays using radar-based techniques and mobile technology. this paper reports on the latest efforts of the mariachi1 program at stony brook university, a unique endeavor that detects and studies ultra-high-energy cosmic rays. this is done by using a novel detection technique based on radar-like technology and traditional scintillator ground detectors. using the phenomena of cosmic rays and meteors as vehicles to motivate research and educational activities, innovative hands-on modules in physics, engineering and cyberinfrastructure based on a learning by doing philosophy are offered to high school teachers and students. participants at all levels are engaged in research projects, seminars, and workshops, where they will learn to use tools needed in mariachi by means of mobile technology.
evolutionary spectrum estimation for uniformly modulated processes with improved boundary performance. the evolutionary spectrum (es) is a time-dependent analogue of the spectrum of a stationary process. existing estimators of the es suffer from bias problems in the boundary region of the time-frequency domain, due to windowing effects. we propose a new estimator of the es of a uniformly modulated process which mitigates these problems. our estimator is based on an extrapolation of the es in time, using an estimate of the time derivative of the es. we apply our estimator to a simulated example of a uniformly modulated process with known es.
target speech extractionwith learned spectral bases. in this paper we present a method for extracting a speech signal of target speaker from noisy convolutive mixtures of target speech and an interference source, when training utterances of the target speaker are available. we incorporate a statistical latent variable model into blind source separation (bss), where we make use of spectral bases learned from the training utterances of the target speaker to identify which source corresponds to the target speaker. combined with any existing bss methods, our post-processing (which is the main contribution) consists of two steps: (1) channel selection where we identify the source corresponding to the target speaker; (2) enhancement where we further suppress the remaining interference. numerical experiments confirm that our method substantially improves the separation quality of existing bss methods and successfully restores the target speaker's speech.
a modified distortion metric for audio coding. current audio coding standards employ the modified discrete cosine transform (mdct) where overlapped frames of audio are windowed and transformed to the frequency domain. encoding parameters are chosen so as to minimize a distortion measure subject to a rate constraint. at the decoder, inverse transformation involves additional windowing and overlap-add of frames. an analysis of the time domain error in the reconstructed frame reveals that distortion metrics based solely on the mdct domain error are in fact unable to capture the effects of windowing and overlap-add at the decoder. the main contribution of this paper is a modified distortion metric that does capture these effects via modified discrete sine transform analysis. when incorporated into an advanced audio coder the proposed distortion metric significantly improves subjective quality of reconstructed audio.
bayesian sparse image reconstruction for mrfm. in this paper, we propose a bayesian model and a monte carlo markov chain (mcmc) algorithm for reconstructing images that consist of only few non-zero pixels. an appropriate distribution that promotes sparsity is proposed as prior distribution for the pixel values. the hyperparameters involved in the modeling are also assigned prior distributions, resulting in a hierarchical model. a gibbs sampler allows us to draw samples distributed according the full posterior of interest. these samples are then used to approximate standard maximum a posteriori (map) estimator. by conducting some simulations, we show that the proposed estimator clearly outperforms previous estimators proposed in the literature.
a hjs filter to track visually interacting targets. visual tracking with explicit occlusion models is computationally hard, in the sense that the complexity explodes as the number of targets increases. recently, the hybrid joint-separable (hjs) model has been proposed that enables tracking the local appearance of a number of bodies through occlusions with a quadratic, no more exponential, upper bound. in this paper we extend that method to account for a larger spectrum of visual interactions, captured by a full-image likelihood enabling true bayesian inference, without compromising scalability. the resulting tracker then proves to be significantly more robust, and able to resolve long term occlusion among five people aligned on a single line-of-sight, observed from a single camera, at a manageable computational cost.
adaptive rhythmic component extractionwith regularization for eeg data analysis. rhythmic component extraction (rce) is a method for extracting a signal oscillating at a certain frequency from multi-channel sensor signals. this method can be effectively used for detecting rhythmic signals such as alpha and beta waves, which are the feature signals in brain computer/machine interfaces (bci/bmi). we are addressing a problem in developing an on-line adaptive algorithm for rce. since a rhythmic signal in the brain slowly varies, the signals extracted in adjacent frames should not largely different. we propose introducing a regularization term that evaluates the correlation between the signal extracted in the last step and the one to be extracted to achieve this. we show that the maximization of the cost function with the proposed regularization term is reduced to a generalized eigenvalue problem and experimental results from practical eeg data support this analysis.
query by tapping system based on alignment algorithm. query-by-tapping systems are content-based music retrieval systems that allow users to tap or clap in a microphone the rhythmic pattern of the melody requested. in this paper, a new query-by-tapping system is described. this system is based on adaptations of alignment algorithms successfully applied for melodic similarity estimation. a similarity score is computed according to the cost of the elementary operations necessary to transform the query into the musical piece tested. a new method for extracting the rhythmic pattern from audio signals is also presented. this method is based on the analysis of the variations of the kurtosis computed from the audio samples. experiments performed on the mirex 2008 database show that the alignment technique proposed performs better than the participating algorithms. they also confirm the interest of the kurtosis-based analysis method in the case of noisy percussive queries.
wrapping snakes for improved lip segmentation. a key step in the process of lip-reading is determining the shape of the speaker's lips. this has previously been achieved through an energy method known as “snakes”, however this approach has some limitations. this paper presents an adapted approach called wrapping snakes, where the image forces are modified based on the snake's location and orientation. this modification encourages wrapping snakes to continue along features they have already partially found, overcoming one of the problems of traditional snakes. the use of wrapping snakes allows for more accurate and robust lip segmentation, as well as increasing the speed of the segmentation.
joint estimation of short-term and long-term predictors in speech coders. in low bit-rate coders, the near-sample and far-sample redundancies of the speech signal are usually removed by a cascade of a short-term and a long-term linear predictor. these two predictors are usually found in a sequential and therefore suboptimal approach. in this paper we propose an analysis model that jointly finds the two predictors by adding a regularization term in the minimization process to impose sparsity constraints on a high order predictor. the result is a linear predictor that can be easily factorized into the short-term and long-term predictors. this estimation method is then incorporated into an algebraic code excited linear prediction scheme and shows to have a better performance than traditional cascade methods and other joint optimization methods, offering lower distortion and higher perceptual speech quality.
pitch bends and tonguing articulation in clarinet physical modeling synthesis. a physical modeling approach is used to investigate playing effects in woodwind instruments. this builds upon prior work concerning both empirical studies of the acoustics of the clarinet and extensive development of computer simulations of musical instrument systems. specifically, explicit implementations of two performance gestures for the clarinet are given and demonstrated: tonguing and pitch bending. physical modeling for the clarinet is briefly reviewed. following this we show how both tonguing and pitch bending map to changes in the clarinet physical model itself and in the control parameter data. to our knowledge, tonguing in particular is one effect that has not been widely discussed in the literature, at least in the exact form we present. finally, some possible future research directions are indicated.
comparison of mpeg-7 descriptors for long term selection of reference frames. during the last years, the amount of multimedia content has greatly increased. this has multiplied the need of efficient compression of the content but also the ability to search, retrieve, browse, or filter it. generally, video compression and indexing have been investigated separately. however, as the amount of multimedia content grows, it will be very interesting to study representations that, at the same time, provide good compression and indexing functionalities. moreover, even if the indexing metadata is created for functionalities such as search, retrieval, browsing, etc., it can also be employed to increase the efficiency of current video codecs. here, we use it to improve the long term prediction step of the h.264/avc video codec. this paper focuses on the comparison between four different mpeg-7 descriptors when used in the proposed scheme.
inversion of short-time fourier transform magnitude spectrograms with adaptive window lengths. in this paper, we extend the real-time iterative spectrogram inversion method (rtisi) for generating a time-domain audio signal from a magnitude spectrogram such that it can handle changing spectrogram window lengths. for each desired window length, we use a separate buffer structure and synchronize the buffers each time the window length changes. this way, the proposed method helps to improve the time/frequency-resolution trade-off for algorithms that operate on magnitude-only spectra.