waashk/dblp huggingface dataset:
cepstral modulation ratio regression (cmrare) parameters for audio signal analysis and classification. in this paper we propose a new set of parameters for audio signal analysis and classification. these parameters are regressions computed on the normalized modulation spectrum of high-resolution cepstral coefficients. the parameter set is scalable in its size and gives a compact representation of the modulation content of speech and other audio signals. these parameters as well as the regression approximation error are well suited for characterizing audio signals in a unified framework. in particular we use a set of eight parameters in a speech/music/noise classification task in which we achieve a classification accuracy which compares very well with other approaches including static and dynamic mfccs.
outage-based designs for multi-user transceivers. we consider a broadcast channel with multiple antennas at the base station and single-antenna receivers, and we study transceiver design with quality of service (qos) requirements in the presence of uncertain channel state information (csi) at the transmitter. each user's qos requirement is formulated as an upper bound on the outage probability of the mean square error (mse), and we demonstrate that these constraints imply bounds on the outage of the received signal-to-interference-plus-noise-ratio. using this mse framework, we provide a unified approach to the design of non-linear and linear transceivers that minimize the transmitted power required to satisfy the qos constraints. we present three conservative design approaches that yield (deterministic) convex and efficiently-solvable design formulations that guarantee the satisfaction of the qos constraints, and we propose computationally-efficient algorithms that can reduce the level of conservatism in the initial formulations.
distributed subgradient projection algorithm for convex optimization. we consider constrained minimization of a sum of convex functions over a convex and compact set, when each component function is known only to a specific agent in a time-varying peer to peer network. we study an iterative optimization algorithm in which each agent obtains a weighted average of its own iterate with the iterates of its neighbors, updates the average using the subgradient of its local function and then projects onto the constraint set to generate the new iterate. we obtain error bounds on the limit of the function value when a constant stepsize is used.
spatial super-resolution of a diffusion field by temporal oversampling in sensor networks. we study the spatial-temporal sampling of a linear diffusion field, and show that it is possible to compensate for insufficient spatial sampling densities by oversampling in time. our work is motivated by the following issue often encountered in sensor network sampling, namely increasing the temporal sampling density is often easier and less expensive than increasing the spatial sampling density of the network. for the case of sampling a diffusion field, we show that, to achieve trade-off between spatial and temporal sampling, the spatial arrangement of the sensors must satisfy certain conditions. we provide in this paper the precise relationships between the achievable reduction of spatial sampling density, the required temporal oversampling rate, the spatial arrangement of the sensors, and the bound for the condition numbers of the resulting sampling and reconstruction procedures.
a mimo channel perturbation analysis for robust bit loading. in narrowband multiple-input multiple-output (mimo) communication systems, when the channel state information (csi) is known perfectly at the transmitter and the receiver, techniques such as waterfilling may use the singular value decomposition to separate the mimo channel into independent single-input single-output subchannels. the signal-to-noise ratios of these subchannels are easily found, and, therefore, so are the subchannel bit allocations. in practice, perfect csi is difficult to obtain. imperfect csi results in subchannel coupling and co-channel interference. in this paper, simple first-order expressions are presented for the signal and interference/noise powers for each subchannel for the imperfect csi case. these expressions may be used to obtain more realistic subchannel bit allocations, allowing for fewer channel outages. numerical simulations demonstrate the applicability and usefulness of the derived expressions.
leveraging multiple query logs to improve language models for spoken query recognition. a voice search system requires a speech interface that can correctly recognize spoken queries uttered by users. the recognition performance strongly relies on a robust language model. in this work, we present the use of multiple data sources, with the focus on query logs, in improving asr language models for a voice search application. our contributions are three folds: (1) the use of text queries from web search and mobile search in language modeling; (2) the use of web click data to predict query forms from business listing forms; and (3) the use of voice query logs in creating a positive feedback loop. experiments show that by leveraging these resources, we can achieve recognition performance comparable to, or even better than, that of a previously deploy system where a large amount of spoken query transcripts are used in language modeling.
inter-tone noise reduction in a low bit rate celp decoder. in this paper we present a novel technique to enhance music signals encoded using a low bit rate celp coder. the method is based on reduction of inter-tone quantization noise for decoded music signals without affecting the quality for speech signals. the proposed technique consists of two modules. the first module is used to discriminate between stable tonal sounds and other sounds and the second module is used to reduce the inter-tone quantization noise in the stable tonal segments. the inter-tone noise is reduced by means of spectral subtraction. the proposed method is a part of the newly standardised itu-t g.718 codec.
steady-state analysis of the normalized least mean fourth algorithm without the independence and small step size assumptions. in this work, the steady-state analysis of the normalized least mean fourth (nlmf) algorithm under very weak assumptions is investigated. no restrictions are made on the dependence between input successive regressors, the dependence among input regressor elements, the length of the adaptive filter, the distribution of noise and the filter input. moreover, in our approach, there is no restriction made on the step size value and therefore the analysis holds for all the values of the step size in the range where the nlmf algorithm is stable. the analysis is based on the effective weight deviation vector performance measure [1]. this vector is the component of weight deviation vector in the direction of the input regressor. the asymptotic time-averaged convergence for the mean square effective weight deviation, the mean absolute excess estimation error, and the mean square excess estimation error for the nlmf algorithm are derived. finally, a number of simulation results are carried out to corroborate the theoretical findings.
a simple design of space-time block codes achieving full diversity with linear receivers. orthogonal space-time block codes (ostbc) are attractive in that they can achieve full diversity and linear complexity of maximum likelihood (ml) decoding. however, the ostbc have a low symbol rate due to the limitation of the orthogonality of the code structure. most of the high-rate stbc achieve full diversity based on ml decoding at the receiver that is computationally expensive. in order to achieve full diversity with linear receivers, recently liu-zhang-wong and shang-xia introduced new stbc. in this paper, we propose a simple design of stbc which have a high rate and achieve full diversity with linear receivers. the proposed stbc are constructed by embedding alamouti codes into a toeplitz matrix. simulation results show that in comparison with some existing codes for a given codeword length the proposed stbc can give a better bit error rate (ber) performance while having a high rate.
a quality prediction model for jpeg2000-based color images. a quality prediction model is proposed for color images coded with jpeg2000. this model estimates the quality (psnr) of a color image at a given compression ratio without coding. the image activity measure as the image feature and compression ratio are taken as the input of the model. experimental results show that the prediction error is less than 1db for more than 75% images, and less than 2db for over 95% images. the computation of the prediction process is much lower than that of the compression and quality calculation.
a fast cabac rate estimator for h.264/avc mode decision. h.264/avc coders use the rate-distortion (r-d) cost function to decide the coding mode for each coding unit for better r-d tradeoff. to evaluate the r-d cost function, both the bit rate and the video quality degradation of the candidate mode must be calculated. in this paper, a fast context-adaptive binary arithmetic coding (cabac) rate estimation scheme is proposed to accelerate the rate calculation. the speed of the proposed rate estimator depends only on the number of contexts used in the coding unit. experimental results show that the proposed rate estimator reduces about 20% of the computational complexity of the r-d optimized mode decision when it is implemented as software. the entire encoder implemented as software is then accelerated by 16% with negligible degradation in the r-d performance. if implemented as hardware, the proposed scheme is expected to accelerate the rate estimation for a macroblock by 5 to 18 times faster than the conventional cabac operation.
generic video coding with abstraction and detail completion. this paper presents a generic video coding framework with the texture abstraction and completion, inspired by a strong grouping bias of local elements in gestalt psychology. abstracting imagery by grouping perceptual salience from anisotropic diffusion, it decomposes video images into two layers composing of semantic components and residual detail. the similarity between textures of abstraction layer is motivated to infer the restoration of missing detail, under the spatio-temporal variation regularity. through a motion and spatial context of moton, hence, a group of pictures (gop) is divided into key frames and abstracted frames to form the final compressed data. an abstraction refinement is tuned to improve matching of detail restoration based on bilateral filtering. the proposed approach is more generic without incurring any specific side information, and achieves up to 20% bit saving versus standard h.264 at similar visual quality levels.
the gigavision camera. we propose a new image device called gigavision camera. the main differences between a conventional and a gigavision camera are that the pixels of the gigavision camera are binary and orders of magnitude smaller. a gigavision camera can be built using standard memory chip technology, where each memory bit is designed to be light sensitive. a conventional gray level image can be obtained from the binary gigavision image by low-pass filtering and sampling. the main advantage of the gigavision camera is that its response is non-linear and similar to a logarithmic function, which makes it suitable for acquiring high dynamic range scenes. the larger the number of binary pixels considered, the higher the dynamic range of the gigavision camera will be. in addition, the binary sensor of the gigavision camera can be combined with a lens array in order to realize an extremely thin camera. due to the small size of the pixels, this design does not require deconvolution techniques typical of similar systems based on conventional sensors.
on ica of improper and noncircular sources. we provide a review of independent component analysis (ica) for complex-valued improper and noncircular random sources. an improper random signal is correlated with its complex conjugate, and a noncircular random signal has a rotationally variant probability distribution. we present methods for ica using second-order statistics, and higher-order statistics. for ica based on second-order statistics, we emphasize the key role played by the circularity coefficients, which are the canonical correlations between the source and the complex conjugate. for ica based on higher-order statistics, we show how to extend algorithms for real-valued ica to the complex domain using wirtinger calculus.
a compressive sensing approach to object-based surveillance video coding. this paper studies the feasibility and investigates various choices in the application of compressive sensing (cs) to object-based surveillance video coding. the residual object error of a video frame is a sparse signal and cs, which aims to represent information of a sparse signal by random measurements, is considered for coding of object error. this work proposes several techniques using two approaches- direct cs and transform-based cs. the techniques are studied and analyzed by varying the different trade-off parameters such as the measurement index, quantization levels etc. finally we recommend an optimal scheme for a range of bitrates. experimental results with comparative bitrates-vs-psnr graphs for the different techniques are presented
on the error exponents for detecting randomly sampled noisy diffusion processes. this paper deals with the detection of a continuous random process described by an ornstein-uhlenbeck (o-u) stochastic differential equation. randomly spaced sensors or equivalently a random time sampler which deliver noisy samples of the process are used for this detection. two types of tests are considered: either h0 refers to the presence of the noisy o-u process or h0 refers to the sole presence of noise. for any fixed false alarm probability, it is shown that the type ii error probability decreases to zero exponentially in the number of samples. the exponents, which do not depend on the false alarm probability, are characterized. this work completes former contributions that consider noiseless o-u process with a random sampling or noisy o-u processes with a regular sampling.
proportionate adaptive algorithm for nonsparse systems based on krylov subspace and constrained optimization. in this paper, we propose an efficient design of proportionality factors in the recently established algorithm named krylov-proportionate normalized least mean-square (kpnlms), which is an extention of the pnlms algorithm to nonsparse (or dispersive) unknown systems by means of a krylov subspace. the designing task takes a form of minimizing the number of iterations that is needed for an upper bound of the system mismatch to reach a specified target value. the minimization is performed under several constraints related to numerical stability, computational requirements, and nonnegativity, and its closed-form solution is derived. numerical examples demonstrate that the proposed design significantly reduces the number of iterations needed to achieve target values of system mismatch especially when a low level of system mismatch is required.
optimal linear fusion for distributed spectrum sensing via semidefinite programming. as an enabling functionality of overlay cognitive radio networks, spectrum sensing needs to reliably detect licensed signal in the band of interest. to achieve reliable sensing, we propose a linear fusion scheme for distributed spectrum sensing to combine the sensing results from multiple spatially distributed cognitive radios. the optimal linear fusion design is formulated into a nonconvex optimization problem. we show that the optimal solution of such a nonconvex problem can be solved via semi-definite programming reformulation.
multichannel speech enhancement using convolutive transfer function approximation in reverberant environments. recently, we have presented a transfer-function generalized sidelobe canceler (tf-gsc) beamformer in the short time fourier transform domain, which relies on a convolutive transfer function approximation of relative transfer functions between distinct sensors. in this paper, we combine a delay-and-sum beamformer with the tf-gsc structure in order to suppress the speech signal reflections captured at the sensors in reverberant environments. we demonstrate the performance of the proposed beamformer and compare it with the tf-gsc. we show that the proposed algorithm enables suppression of reverberations and further noise reduction compared with the tf-gsc beamformer.
speaker diarization in meeting audio. this paper describes speaker diarization system on a nist rich transcription 2007 (rt-07) meeting recognition evaluation data set for the task of multiple distant microphone (mdm). our implementation includes three components: initial clustering, non-speech removal and cluster purification. initial clusters are generated using directional of arrival (doa) information and bootstrap clustering. multiple gmm modeling for speech/non-speech classification is employed for non-speech removal component. in addition, a novel system fusion strategy using information from receiver operating curve (roc) is proposed for non-speech removal component. finally, consensus clustering approach together with iterative gmm clustering method is employed for speaker cluster purification. the system achieves the overall der of 10.81%.
the effect of language factors for robust speaker recognition. from the results of the nist speaker recognition evaluation in resent years, speaker recognition systems which are mainly developed based on english training data suffer the language gap problem, namely, the performance of non-english trails is much worse than that of english trails. this problem is addressed in this paper. based on the conventional joint factor analysis model, we enrolled in the language factors which are mean to capture the language character of each testing and training speech utterance, and compensation was carried out by removing the language factors in order to shrink the difference between languages. experiments on 2006 nist sre data show that, the language factor compensation alone can reduce the gap between the performance of english and non-english trails, and the score level combination with eigenchannels can further improve the performance of non-english trails, e.g., for female part, we observed about 19% relatively reduction in eer, when compared with eigenchannels session variability compensation alone.
generalization of specialized on-the-fly composition. in the weighted finite state transducer (wfst) framework for speech recognition, we can reduce memory usage and increase flexibility by using on-the-fly composition which generates the search network dynamically during decoding. methods have also been proposed for optimizing wfsts in on-the-fly composition, however, these operations place restrictions on the structure of the component wfsts. we propose extended on-the-fly optimization operations which can operate on wfsts of arbitrary structure by utilizing a filter composition. the evaluations illustrate the proposed method is able to generate more efficient wfsts.
evolution of social p2p networks based on the dynamics of heterogeneous multimedia peers. in this paper, we consider social peer-to-peer (p2p) networks, where peers are sharing their resources (i.e., multimedia content and upload bandwidth). in the considered p2p networks, peers are self-interested, thereby determining their resource divisions (i.e., actions) among their associated peers such that their utility (e.g., multimedia quality) is maximized. peers determine their optimal strategies for selecting their action based on a markov decision process (mdp) framework, which enables the peers to maximize their cumulative utilities. we consider heterogeneous peers that have different and limited ability to characterize their resource reciprocations using only a limited number of states. we investigate how the limited number of states impacts the resource reciprocation and the resulting multimedia quality over time. simulation results show that peers simultaneously refining their state descriptions can improve the multimedia quality in the resource reciprocation. moreover, peers prefer to interact with other peers that have higher available upload bandwidths as well as have similar capabilities for refining their number of states.
a new optimization method for reference-based quadratic contrast functions in a deflation scenario. this paper deals with the problem of blind source separation of convolutive mimo mixtures by a deflation procedure. contrast functions showing a quadratic dependence with respect to the searched parameters have recently been proposed. combined with a fast svd-based optimization technique, they proved to be very efficient for the extraction of one source signal. in this contribution, we examine how these contrast functions behave in a deflation scenario. we show that the svd-based optimization method requires a good knowledge of the filter orders due to its sensitivity on a rank estimation. to overcome the difficulty, we propose an optimal step size gradient algorithm.
nonlinear acoustic echo control using an accelerometer. a typical echo canceller uses an fir adaptive filter to estimate the impulse response of the near-end echo path. however nonlinear echo components due to the loudspeaker and driving amplifier are not captured by such an arrangement. we propose a novel scheme for nonlinear acoustic echo cancellation where an accelerometer sensor captures loudspeaker vibration and this sensor signal is used along with the error signal to adapt the adaptive filter coefficients. the accelerometer sensor is used in lieu of explicit nonlinear modeling in the echo cancelling loop and hence it is compatible with typical nlms and rls echo cancellation algorithms. experiments with an accelerometer mounted on the magnet of a hands-free kit show an echo reduction improvement by as much as 15 db in situations of nonlinear loudspeaker distortion.
a novel algorithm for calculating the qr decomposition of a polynomial matrix. a novel algorithm for calculating the qr decomposition (qrd) of polynomial matrix is proposed. the algorithm operates by applying a series of polynomial givens rotations to transform a polynomial matrix into an upper-triangular polynomial matrix and, therefore, amounts to a generalisation of the conventional givens method for formulating the qrd of a scalar matrix. a simple example is given to demonstrate the algorithm, but also illustrates two clear advantages of this algorithm when compared to an existing method for formulating the decomposition. firstly, it does not demonstrate the same unstable behaviour that is sometimes observed with the existing algorithm and secondly, it typically requires less iterations to converge. the potential application of the decomposition is highlighted in terms of broadband multi-input multi-output (mimo) channel equalisation.
decentralized dynamic spectrum allocation based on adaptive antenna array interference mitigation diversity: algorithms and markov chain analysis. decentralized dynamic spectrum allocation (dsa) that exploit adaptive antenna array interference mitigation (im) diversity at the receiver, is proposed for interference-limited environments with high level of frequency reuse. the system consists of base stations (bss) that may belong to different providers, who can optimize uplink frequency allocation to their subscriber stations (sss) to achieve the least impact of im on the useful signal, assuming no control over band allocation of other bss sharing the same bands. “selfish” and “good neighbor” decentralized dsa strategies are considered. convergence and convergence rate of the introduced techniques are investigated by means of the theory of absorbing markov chains.