waashk/dblp huggingface dataset:
incorporating spectral subtraction and noise type for unvoiced speech segregation. unvoiced speech poses a big challenge to current monaural speech segregation systems. it lacks harmonic structure and is highly susceptible to interference due to its relatively weak energy. this paper describes a new approach to segregate unvoiced speech from nonspeech interference. the system first estimates a voiced binary mask, and then performs unvoiced speech segregation in two stages: segmentation and grouping. in segmentation, time-frequency units labeled as 0 in the voiced binary mask are first used to estimate the noise energy and spectral subtraction is then performed to generate time-frequency segments in unvoiced intervals. based on the type of noise, unvoiced segments are grouped either by selecting segments consistent with those generated by onset/offset analysis or by bayesian classification of acoustic-phonetic features. systematic evaluation and comparison show that the proposed approach improves the performance of unvoiced speech segregation considerably.
training and adapting mlp features for arabic speech recognition. features derived from multi-layer perceptrons (mlps) are becoming increasingly popular for speech recognition. this paper describes various schemes for applying these features to state-of-the-art arabic speech recognition: the use of mlp-features for short-vowel modelling in graphemic systems; rapid discriminative model training by standard plp feature lattice re-use; and mlp feature adaptation using linear input networks (lin). the use of rapid training using mlp features and their use for short-vowel modelling and lin adaptation gave reductions in word error rate. however significant improvements over explicit short-vowel modelling with standard multi-pass adaptation were not obtained, although they were useful in combination.
blind monte carlo detection-estimation method for optical coherence tomography. we consider the parametric analysis of frequency-domain optical coherence tomography (oct) signals. a monte carlo (gibbs sampler) detection-estimation method for determining the depths and reflection coefficients of tissue interfaces (reflective sites in the tissue) is proposed. our method is blind since it estimates the instrumentation-dependent “fringe” function along with the tissue parameters. sparsity of the detected interfaces is enforced by an impulse detector and a modified bernoulli-gaussian prior with a minimum distance constraint. numerical results using synthetic and real signals demonstrate the excellent performance and fast convergence of our method.
marginalized population monte carlo. population monte carlo is a statistical method that is used for generation of samples approximately from a target distribution. the method is iterative in nature and is based on the principle of importance sampling. in this paper, we show that in problems where some of the parameters are conditionally linear on the remaining parameters, we can improve the computational efficiency of population monte carlo by generating samples of the nonlinear parameters only and marginalizing the linear parameters. we demonstrate the marginalized population monte carlo on the problem of frequency estimation of closely spaced sinusoids.
optimized non-uniform fast fourier transform (nufft) for iterative tomographic reconstruction. the main focus of this paper is the efficient approximation of the non-uniform fourier transform (nufft). we reformulate the standard nufft approximation as a projection of the exact discrete fourier transform onto a shift-invariant space. this reformulation enables the use of sophisticated tools, developed in the context of shift-invariant representations, to analyze the performance of the approximation. using these techniques, we derive the optimal scale factors for a specified interpolator. assuming these scale factors, we develop a worst-case error criterion that is only dependent on the interpolating function. we propose an iterative re-weighted optimization algorithm to derive the optimized least square (ols) interpolator. this interpolator significantly reduces the approximation error in comparison to the standard methods. the improved performance of this scheme, for low oversampling factors, could lead to a memory efficient algorithm for non-cartesian fourier inversion.
mpeg-4 scalable lossless audio transparent bitrate and its application. in this paper, the relevance between the bit-plane levels and the perceptual quality of the mpeg-4 scalable lossless audio is explored. it is observed that only the top 3 bit-planes in sls with aac core of 64kbps are closely related to the transparent quality. the result is used in the application of the data hiding process. the proposed hiding method has low complexity as no side information is required at the data extraction process. a data hiding capacity of 98kbps is achieved for sls lossless bitstream.
acoustic holography with a concentric rigid and open spherical microphone array. we present a new method and performance data related to volumetric acoustic intensity imaging using a spherical microphone array (sma) consisting of a dual, concentric rigid and open sma. the dual, concentric array was designed to improve the frequency range of a standard sma. we apply standard techniques associated with interior spherical near-field acoustic holography (nah) and, in particular, consider issues related to the optimal use of information from both arrays for nah projection and the advantages that thus accrue from utilising a dual, concentric sma.
low power embedded speech recognition system based on a mcu and a coprocessor. in speech recognition systems, chmm (continuous hidden markov model) based speech recognition algorithms have the best accuracy but with the most computational cost. neither general purpose processor (gpp) nor dedicated hardware implementation is a good solution for the algorithm, due to high power consumption for the former and lack of flexibility for the later. to reduce power consumption and enhance flexibility, this paper presents a speech recognition system composed of a coprocessor and a mcu. the coprocessor is a dedicated hardware design for output probability calculation (opc), which is the most computation-intensive part in chmm, and mcu is a 32bit risc (arm). tested with a 358-state 3-mixture 27-feature 800-word hmm, mcu operates at 40mhz and coprocessor operates at 10mhz to meet real-time requirement. the power consumption of mcu is 10mw, and coprocessor 1.8mw.
a new approach for modelling the dynamic feedback path of digital hearing aids. this paper proposes a reflection model for the dynamic feedback path of digital hearing aids and compares it with two existing models: a direct model and an initialization model, based on the measured dynamic feedback paths. the comparison shows that the proposed model is superior to the existing two models in terms of maximum stable gain (msg). for hearing aids with dual microphones, the possibility of relating the two dynamic feedback paths is also investigated. it is shown that in a complicated acoustic environment, the relation between the two feedback paths can be very intricate and difficult to exploit in modelling the dynamic feedback paths.
rls-weighted lasso for adaptive estimation of sparse signals. the batch least-absolute shrinkage and selection operator (lasso) has well-documented merits for estimating sparse signals of interest emerging in various applications, where observations adhere to parsimonious linear regression models. to cope with linearly growing complexity and memory requirements that batch lasso estimators face when processing observations sequentially, the present paper develops a recursive lasso algorithm that can also track slowly-varying sparse signals of interest. performance analysis reveals that recursive lasso can either estimate consistently the sparse signal's support or its nonzero entries, but not both. this motivates the development of a weighted version of the recursive lasso scheme with weights obtained from the recursive least-squares (rls) algorithm. the resultant rls-weighted lasso algorithm provably estimates sparse signals consistently. simulated tests compare competing alternatives and corroborate the performance of the novel algorithms in estimating time-invariant and tracking slow-varying signals under sparsity constraints.
bounded conditional mean imputation with gaussian mixture models: a reconstruction approach to partly occluded features. in this work we show how conditional mean imputation can be bounded through the use of box-truncated gaussian distributions. that is of interest when signals or features are partly occluded by a superimposed interference, as then the noisy observation poses an upper bound. unfortunately, the occurring integrals are not analytic. hence an approximate solution has to be used. in the experimental section we apply the bounded approach to the reconstruction of partly occluded speech spectra and demonstrate its superiority over the unbounded case with respect to automatic speech recognition performance.
recent advances in sri's iraqcomm iraqi arabic-english speech-to-speech translation system. we summarize recent progress on sri's iraqcomm™ iraqi arabic-english two-way speech-to-speech translation system. in the past year we made substantial developments in our speech recognition and machine translation technology, leading to significant improvements in both accuracy and speed of the iraqcomm system. on the 2008 nist-evaluation dataset our twoway speech-to-text (s2t) system achieved 6% to 8% absolute improvement in bleu in both directions, compared to our previous year system [1].
pilot design for multi-user mimo. in this paper, challenges regarding the provision of channel state information (csi) on a multi-user (mu) mimo-ofdm downlink are addressed. reference symbols (pilots) support coherent detection at the receiver and enable link adaptation at the transmitter, but add overhead and consume transmit power. as spatial transmit processing combined with multiple access essentially requires the provision of dedicated and common pilots, i.e. pilots that include respectively exclude user-specific transmit processing, the potential gains of mu-mimo may partly be canceled by the incurred pilot overhead. fortunately, spatial correlation at the transmitter has the potential to substantially reduce overheads, by introducing spatial reuse of dedicated pilots, and by utilizing spatial interpolation of common pilots. we demonstrate that a reduction in overhead is traded with compromised channel estimation accuracy, which particularly limits the attainable spectral efficiency in the high snr regime. this implies that a bandwidth efficient pilot design is to be complemented by advanced channel estimation schemes, which enhance accuracy by utilizing data symbols together with pilots.
source adaptive blind signal extraction using closed-form ica for hands-free robot spoken dialogue system. in this paper, we propose a new ica-based bss algorithm including estimation of sources' probability density functions (pdfs) to adapt the nonlinear activation function to various noise conditions. in the proposed method, closed-form second-order ica is introduced as a computational-cost-efficient preprocessing to extract sources' pdfs, which is beneficial for real-time application. compared with various type of conventional icas, e.g., fixed activation-function type and ml-based type, our proposed algorithm can give a faster and higher convergence. based on the proposed source-adaptive ica, we show a real-time noise reduction results under diffuse noise environment. also we can demonstrate our recently developed hands-free robot spoken dialogue system via real-time ica.
dynamic updating and downdating matrix svd and tensor hosvd for adaptive indexing and retrieval of motion trajectories. motion information is regarded as one of the most important cues for developing semantics in video data. yet it is extremely challenging to build indexing and browsing tools for video data, particularly when it involves interactive motions of multiple objects. the problem is further complicated when the video archives are dynamically updated, and/or queries contains partial information. an efficient solution would require that the feature space used to represent the data be dynamically updated or downdated to allow frequent additions/deletions and matching of queries. assuming tensor hosvd as the feature space, in this paper, we propose two novel algorithms, namely, dynamic tensor hosvd updating algorithm (dtsv d+) and dynamic tensor hosvd downdating algorithm (dtsv d−), for dynamically updating and downdating existing tensor hosvd, without recalculating it from the raw data. the proposed algorithms are robustly applied to both full and partial multiple motion trajectories events with varying number of objects, trajectory lengths, and sampling rates. simulations on real-world multiple motion trajectories data demonstrate the robustness and accuracy of the proposed algorithms.
maximum margin linear kernel optimization for speaker verification. this paper describes a novel approach for discriminative modeling and its application to automatic text-independent speaker verification. this approach maximizes the margin between the model scores for pairs of utterances belonging to the same speaker and for pairs of utterances belonging to different speakers. a low-dimensional linear kernel is estimated which maximizes this margin. this approach emphasizes features which have a better ability to discriminate between scores belonging to pairs of utterances of the same target speakers and those of different speakers. in this paper, we apply this approach to the nist 2005 speaker verification task. compared to the gaussian mixture model (gmm) baseline system, a 17.7% relative improvement in the minimum detection cost function (dcf) and a 11.7% relative improvement in equal error rate (eer) are obtained. we achieve also a 5.7% relative improvement in eer and 2.3% relative improvement in dcf by using our approach on top of a nuisance attribute projection (nap) compensated gmmbased kernel baseline system.
application of characteristic function to detection in sinusoidal interference plus gaussian noise. in this work, a detector scheme for the detection of signal in a group of non-gaussian narrowband interferences and white gaussian noise is developed. since there exists no closed-form probability distribution for this type of disturbance modeling, the key innovation lies in the use of characteristic function rather than the probability distribution to both design and implement the detector. parameter estimation is performed at first step to find the unknown disturbance parameters. the utilized detector uses these parameters to form an approximately gaussian distributed test statistic based on the empirical characteristic function of received data. performance of the detector is investigated by means of both analytical and monte carlo simulations.
improved morphological decomposition for arabic broadcast news transcription. in this paper, we show the progress for arabic speech recognition by incorporating contextual information into the process of morphological decomposition. the new approach achieves lower out-of-vocabulary and word error rates when compared to our previous work, in which the morphological decomposition relies on word-level information only. we also describe how the vocalization procedure is improved to produce pronunciations for some dialect arabic words. by using the new approach, we reduced the word error by 0.8% absolute (4.7% relative) when compared to the baseline approach.
exploiting prosodic information for speaker recognition. in this paper, we study speaker characterization using prosodic supervectors with negative within-class covariance normalization (nwccn) projection and speaker modeling with support vector regression (svr). we also propose a segmental weight fusion (swf) technique that combines acoustic and prosodic subsystems effectively, despite the big performance gap between the subsystems. we validate the effectiveness of our proposed techniques on the nist 2006 speaker recognition evaluation (sre) in comparison with other prominent solutions. the experiments have reported competitive results of 17.72% equal error rate for the prosodic subsystem alone and 4.50% for the fusion system on nist 2006 sre core test condition.
a noiseless code length method (nclm) to estimate dimensionality of hyperspectral data. hyperspectral image analysis has been subjected to many improvements made in past decade. yet the accurate estimation of dimensionality is still a challenge. since dimension estimation of the hyperspectral data is the first step in analysis of an image, the accuracy of analysis results highly depends on the accuracy of the dimension estimation step. mostly, existing methods isolate the process of dimension estimation and process of denoising which leads to an inaccurate estimation of constituent components in the signal. in this paper, the problem of estimating the dimensionality of hyperspectral data using the concept of “noiseless code length” is addressed. in our proposed method, nclm, a set of nested subsets including the hyperspectral data is generated first and then an error comparison approach is utilized by estimating the noiseless data error rather than noisy data error used by the existing methods to find the optimum subset. it has been shown that the estimated noiseless error has a minimum that represents the accurate estimation of the dimensionality of hyperspectral data. the comparison of nclm to other methods shows a substantial improvement in estimation of dimensionality in hyperspectral imagery.
multi-level diffusion adaptive networks. we study the problem of distributed estimation, where a set of nodes are required to collectively estimate some parameter of interest from their measurements. diffusion algorithms have been shown to achieve good performance, increased robustness and are amenable for real-time implementations. in this work we focus on multi-level diffusion algorithms, where a network running a diffusion algorithm is enhanced by adding special nodes that can perform different processing. these special nodes form a second network where a second diffusion algorithm is implemented. we illustrate the concept using diffusion lms, provide performance analysis for multi-level collaboration and present simulation results showing improved performance over conventional diffusion.
subband nonstationary noise reduction based on multichannel spatial prediction under reverberant environments. we propose a novel non-stationary and convolutive noise reduction method under reverberant environments. unlike many multichannel noise reduction methods, the proposed method does not need pre knowledge of impulse response or direction of arrival (doa) of the target source. the proposed method is composed of two processes. on the noise reduction process, the noise component is reduced without the impulse response of the target source. the target source component in the output signal is distorted, but the distortion is removed by the distortion-restoration process. importantly, possibility of complete noise reduction with no distortion based on the proposed framework is assured by mint theory. experimental results under the reverberant environment (rt60 ≈ 300 ms) show that the proposed method can reduce more noise than the conventional method and the distortion of the target source is not so big.
extraction of velocity fields for geophysical fluids from a sequence of images. we consider the assimilation of satellite images, within the framework of data assimilation in geophysical systems. based on the constant brightness assumption, we define a nonlinear functional measuring the difference between two consecutive images, the first one being transported to the second one by the unknown velocity. by considering a multi-scale approach and a gauss-newton minimization algorithm, we can estimate the entire velocity fields at a high frame rate and then assimilate these pseudo-observations.
a hardware-efficient implementation of the fast affine projection algorithm. this paper presents a high-throughput, low-latency, hardware-efficient fixed-point implementation of the fast affine projection (fap) algorithm. the proposed architecture utilizes reusable distributed arithmetic (rda) in combination with optimizations in the update process to enable the coefficients to be updated in a fixed number of cycles independent of filter length. fixed-point simulations show that the effect of replacing some of the multiplications with arithmetic shifts is minor, with rda-fap maintaining a faster convergence rate than nlms. the proposed design is also compared against a multiplier-based design in terms of number of computations and number of clock cycles needed for a single fap update cycle.
quantizer noise benefits in nonlinear signal detection with alpha-stable channel noise. two new theorems show how deliberately adding quantizer noise can improve statistical signal detection in array-based nonlinear correlation detection even in the case of infinite-variance α-stable channel noise. the first theorem gives a necessary and sufficient condition for such quantizer noise to increase the detection probability for a fixed false-alarm probability. the second theorem shows that the array must contain more than one quantizer for a stochastic-resonance noise benefit and that the noise benefit improves in the small-quantizer noise limit as the number of array quantizers increases. it further shows that symmetric uniform quantizer noise gives the optimal noise benefit among all symmetric scale-family noise types.
singing voice detection in music tracks using direct voice vibrato detection. in this paper we investigate the problem of locating singing voice in music tracks. as opposed to most existing methods for this task, we rely on the extraction of the characteristics specific to singing voice. in our approach we suppose that the singing voice is characterized by harmonicity, formants, vibrato and tremolo. in the present study we deal only with the vibrato and tremolo characteristics. for this, we first extract sinusoidal partials from the musical audio signal . the frequency modulation (vibrato) and amplitude modulation (tremolo) of each partial are then studied to determine if the partial corresponds to singing voice and hence the corresponding segment is supposed to contain singing voice. for this we estimate for each partial the rate (frequency of the modulations) and the extent (amplitude of modulation) of both vibrato and tremolo. a partial selection is then operated based on these values. a second criteria based on harmonicity is also introduced. based on this, each segment can be labelled as singing or non-singing. post-processing of the segmentation is then applied in order to remove short-duration segments. the proposed method is then evaluated on a large manually annotated test-set. the results of this evaluation are compared to the one obtained with a usual machine learning approach (mfcc and sfm modeling with gmm). the proposed method achieves very close results to the machine learning approach : 76.8% compared to 77.4% f-measure (frame classification). this result is very promising, since both approaches are orthogonal and can then be combined.