waashk/dblp huggingface dataset:
sparse imputation for noise robust speech recognition using soft masks. in previous work we introduced a new missing data imputation method for asr, dubbed sparse imputation. we showed that the method is capable of maintaining good recognition accuracies even at very low snrs provided the number of mask estimation errors is sufficiently low. especially at low snrs, however, mask estimation is difficult and errors are unavoidable. in this paper, we try to reduce the impact of mask estimation errors by making soft decisions, i.e., estimating the probability that a feature is reliable. using an isolated digit recognition task (using the aurora-2 database), we demonstrate that using soft masks in our sparse imputation approach yields a substantial increase in recognition accuracy, most notably at low snrs.
temporally variable multi-aspect auditory morphing enabling extrapolation without objective and perceptual breakdown. a generalized framework of auditory morphing based on the speech analysis, modification and resynthesis system straight is proposed that enables each morphing rate of representational aspects to be a function of time, including the temporal axis itself. two types of algorithms were derived: an incremental algorithm for real-time manipulation of morphing rates and a batch processing algorithm for off-line post-production applications. by defining morphing in terms of the derivative of mapping functions in the logarithmic domain, breakdown of morphing resynthesis found in the previous formulation in the case of extrapolations was eliminated. a method to alleviate perceptual defects in extrapolation is also introduced.
sparse lms for system identification. we propose a new approach to adaptive system identification when the system model is sparse. the approach applies ℓ1 relaxation, common in compressive sensing, to improve the performance of lms-type adaptive methods. this results in two new algorithms, the zero-attracting lms (za-lms) and the reweighted zero-attracting lms (rza-lms). the za-lms is derived via combining a ℓ1 norm penalty on the coefficients into the quadratic lms cost function, which generates a zero attractor in the lms iteration. the zero attractor promotes sparsity in taps during the filtering process, and therefore accelerates convergence when identifying sparse systems. we prove that the za-lms can achieve lower mean square error than the standard lms. to further improve the filtering performance, the rza-lms is developed using a reweighted zero attractor. the performance of the rza-lms is superior to that of the za-lms numerically. experiments demonstrate the advantages of the proposed filters in both convergence rate and steady-state behavior under sparsity assumptions on the true coefficient vector. the rza-lms is also shown to be robust when the number of non-zero taps increases.
on noise reduction in the karhunen-loève expansion domain. in this paper, we study the noise-reduction problem in the karhunen-loève expansion domain. we develop two classes of optimal filters. the first class estimates a frame of speech by filtering the corresponding frame of the noisy speech. we will show that several well-known existing methods belong or are closely related to this category. the second class, which has not been studied before, obtains noise reduction by filtering not only the current frame, but also a number of previous consecutive frames of the noisy speech. we will discuss how to design the optimal noise-reduction filters in each class and demonstrate the properties of the deduced optimal filters.
arx-lf-based source-filter methods for voice modification and transformation. two arx-lf-based source/filters models for speech signals are presented. a robust glottal inversion technique is used to deconvolve the signal into an excitation component and a filter component. the excitation component is further decomposed into an lf part and a residual part. the first model, referred to as the lf-vocoder, is a high quality vocoder that replaces the residual part with modulated noise. the second model uses a sinusoidal harmonic representation of the residual signal. the latter does not degrade the signal during analysis/synthesis and provides higher quality for small modification factors, while the former has the advantage of being a compact, fully parametric representation that is suitable for low-bit-rate speech coding as well as parametric speech synthesis applications.
near-lossless compression and protection by turbo source-channel (de-)coding using irregular index assignments. in this paper, we present a novel near-lossless compression scheme for scalar-quantized source codec parameters. the scheme is comparable to a turbo source coding approach and can inherently incorporate protection against transmission errors. we show that using the concept of exit charts and irregular codes, a linear programming optimization problem can be formulated and the solution of this problem leads to an irregular index assignment offering the best possible compression given the considered system model and a fixed channel quality. the performance of the compression scheme is demonstrated by a simulation example.
a simple iterative algorithm for range-based localization. the range-based localization problem often arises in toa or rssi based position estimation schemes. it is well-known that such a localization problem can be formulated as a nonlinear least-squares (nls) estimation problem. in this paper, we formulate the problem as a constrained optimization problem, which is equivalent to the general nls problem. by using a greedy optimization strategy, we derive a simple iterative algorithm with closed-form expressions for the nls localization, which can be implemented in a distributed way. simulation results show that the localization performance of the proposed localization algorithm is very close to the cramer-rao lower bound.
embedded celp with adaptive codebooks in enhancement layers and multi-layer gain optimization. the paper describes an embedded celp coder in which an adaptive codebook is included in every enhancement layer and the lower-layer codebook gains are re-optimized in the higher layers to further improve speech quality. each layer maintains its own filter memories to generate required target vectors, adds adaptive and fixed codebook contributions, and re-optimizes all codebook gains to improve coder performance (multi-layer gain optimization). the common elements across the embedded layers include the lower-layer adaptive and fixed codebook entries. the pitch-lag used in the core layer is also re-used in the enhancement layers to maintain time synchronization between layers. estimation and encoding of selected lower-layer parameters may take into account their estimated impact on the higher layers. the described embedded celp coder has been implemented in the embedded variable bit-rate (ev-vbr) codec standardized by itu-t as recommendation g.718. the characterization test results of the g.718 embedded celp are summarized.
a quantitative evaluation for 3d face reconstruction algorithms. in this work, we proposed to use quantitative method to evaluate the accuracy of 3d face reconstruction algorithms. the reconstructed 3d faces are first aligned to the ground truth by iterative closest point (icp) algorithm and then the shape difference between the two 3d faces is described by signal to noise ratio (snr). finally, the error maps (em) illustrated the reconstruction errors on corresponded vertices in different dimensions. comparing with the subjective and indirect evaluation methods, the proposed method provides more precise and detailed evaluations for face shape reconstruction. based on the snr, different 3d face reconstruction algorithms can be compared directly and the em also can suggest guidance for feature extraction.
single-channel speech separation and recognition using loopy belief propagation. we address the problem of single-channel speech separation and recognition using loopy belief propagation in a way that enables efficient inference for an arbitrary number of speech sources. the graphical model consists of a set of n markov chains, each of which represents a language model or grammar for a given speaker. a gaussian mixture model with shared states is used to model the hidden acoustic signal for each grammar state of each source. the combination of sources is modeled in the log spectrum domain using non-linear interaction functions. previously, temporal inference in such a model has been performed using an n-dimensional viterbi algorithm that scales exponentially with the number of sources. in this paper, we describe a loopy message passing algorithm that scales linearly with language model size. the algorithm achieves human levels of performance, and is an order of magnitude faster than competitive systems for two speakers.
ranging signal design and its detection for ofdma systems. a single-symbol based initial ranging signal structure and the associated signal detection and timing synchronization methods for ofdma systems are presented. the proposed structure makes it feasible and flexible for a receiver to detect single and multiple ranging codes and estimate the corresponding timing offsets. our approach offers improved performance and enhanced robustness against multi-user interference and multi-path fading. numerical results verify the effectiveness of the proposed method and its advantages over existing alternatives.
jamming mitigation using space-time coded collision-free frequency hopping. frequency hopping (fh) system, which is robust under jamming interference, was originally developed for secure military communication applications. however, the efficiency of the conventional fh scheme is very low due to inappropriate use of the total available bandwidth and transmission collisions. to improve the system capacity, we develop a space-time coded collision-free frequency hopping (stc-cffh) system based on the ofdm framework. the capacity and performance analysis of the proposed scheme is presented under frequency selective fading and partial-band jamming through both theoretical analysis and simulation examples. our analysis indicates that the stc-cffh scheme improves the spectral efficiency and inherent anti-jamming features of conventional fh systems.
introduction of quality measures in audio-visual identity verification. audiovisual identity verification exploits both image and audio information to improve the performance of the identification system. unfortunately, both image and audio systems are sensitive to signal quality. in this paper, we propose a method to combine output classifiers based on both image and audio quality measures. we define classes of signal degradation within which we estimate the fusion weights and normalization parameters. results of experiments on the banca database show that fusion using quality measures improves verification performance by 25% compared to the baseline fusion method.
duality between widely linear and dual channel adaptive filtering. we address the duality between adaptive filtering in ℂ and ℝ2 and provide a comparison between the well understood dual channel real valued least mean square (dcrlms) algorithm in ℝ2 and the corresponding algorithms in ℂ. these include the complex lms (clms) and the recently introduced augmented clms (aclms), a widely linear algorithm designed for the processing of noncircular complex valued signals. the analysis shows that the standard clms and dcrlms in general provide different adaptive filtering solutions, whereas the aclms and dcrlms are isomoprhic and can be made equivalent. the analysis is supported by simulations on noncircular real world signals.
phoneme cluster based state mapping for text-independent voice conversion. this paper takes phonetic information into account for data alignment in text-independent voice conversion. hidden markov models are used for representing the phonetic structure of training speech. states belonging to same phoneme are grouped together to form a phoneme cluster. a state mapped codebook based transformation is established using information on the corresponding phoneme clusters from source and targets speech and weighted linear transform. for each source vector, several nearest clusters are considered simultaneously while mapping in order to generate a continuous and stable transform. experimental results indicate that the proposed use of phonetic information increases the similarity between converted speech and target speech. the proposed technique is applicable to both intra-lingual and cross-lingual voice conversion.
audio-based automatic management of tv commercials. although tv commercial identification and clustering are suitable applications for automatic multimedia indexing technology, they remain as problems still unsolved. most current systems either require a big computational load and therefore can not be executed online, or just perform a detection, without clustering nor identification. in this paper two advertisement indexing approaches are presented: an off-line detection and clustering system and an online identification system, both based only on audio features for computational reasons. for the off-line clustering two metrics are evaluated, and an initial commercial boundary detection algorithm, based on identifying drop energy points which are also acoustic change boundaries, is presented. for the on-line system we analyze the response-time/identification scores constraints. experiments performed on real data validate both off-line and on-line implementations as well as that audio only features are enough discriminant to detect and classify tv commercials.
estimation of the hyperspectral tucker ranks. in hyperspectral image analysis, one often assumes that observed pixel spectra are linear combinations of pure substance spectra. unmixing a hyperspectral image consists in finding the number of pure substances in the scene, finding their spectral signatures and estimating the abundance fraction of each pure substance spectrum in each spectral pixel. in this paper, we show that the tensor tucker decomposition could be considered to solve this problem, and a preliminary problem to overcome consists in estimating the 3 required data tucker ranks, corresponding to the 3 dimensions of the data cube. then, we propose an optimal method to estimate them.
digital beamforming using a gpu. in this paper we investigate the use of gpus as digital beamformers. we specify a parallel implementation of a beamformer in time and frequency domain and measure its performance. we also give examples of the processing limits of nvidia geforce 8800 gpu with respect to application parameters: number of sensors, sampling frequency, bandwidth, and number of simultaneous beams. the results are compared to those of algorithms similarly implemented on a intel xeon cpu. we find that the gpu is able to process a larger amount of information than the cpu, and that it can be used as a digital beamformer for arrays with a large number of elements sampled at high rates. exact results are given for the abovementioned application parameters.
optimal generalized design of transform-based block digital filters. transform-based block implementation of digital filters is useful for high throughput filtering due to inherent parallelism and complexity reduction provided by using the fast transforms. in basic form, for example the overlap-save implementation, the block digital filter (bdf) is represented by a vector. in this paper, the basic form of block filtering and the optimal design of bdf are described. therefore, we propose a generalization of the block digital filtering where the bdf is represented by a matrix. this generalized form and its corresponding optimal bdf design are developed. the generalized bdf allows reducing the global distortion of the block filtering.
control of prosodic focus in corpus-based generation of fundamental frequency contours of japanese based on the generation process model. a total corpus-based process of generating prosodic features from text is developed. the process first predicts pauses and phone durations, and then generates f0 contours. since f0 contour generation is based on the generation process model, it is rather easy to manipulate the generated f0 contours in command level. a method was developed for generating sentence f0 contours, when a focus is placed in one of the “bunsetsu” of an utterance. the method is to predict differences in the f0 model commands between with and without focus utterances, and apply them to the f0 model commands predicted beforehand by the baseline method. the validity of the method was proved by the experiment on f0 contour generation and speech synthesis.
using phase linearity in frequency-domain ica to tackle the permutation problem. this paper describes a method for solving the permutation problem in the frequency-domain independent component analysis (fd-ica) approach to blind source separation (bss). fd-ica is a well-known method for bss of convolutive mixtures. however, fd-ica has a source permutation problem, where estimated source components can become swapped at different frequencies. many researchers have suggested methods to solve the source permutation problem including using correlation between adjacent frequencies and direction of arrival (doa). we propose a modification to the doa method, based on phase linearity of the fd-ica de-mixing matrix, that can extend the range of frequencies over which the permutation problem can be resolved. experiments indicate that our method can provide a better performance than the inter-frequency correlation method and the doa method in real environments.
transmit beamforming for wireless multicasting using channel orthogonalization and local refinement. the problem of transmit beamforming for single-group multicasting is considered, where the objective is to transmit common information to a (large) number of users. the transmitter is assumed to have accurate downlink channel state information (csi) for all users, and the objective is to design the beamformer weights to minimize the total transmitted power subject to meeting the quality-of-service (qos) constraints of all users. this is an np-hard problem that has recently drawn considerable interest (e.g., in the context of umts-lte / e-mbms). several channel orthogonalization-based methods are proposed to solve this problem in an approximate way. our techniques are shown to offer an improved performance-to-complexity tradeoff as compared to the original semidefinite relaxation (sdr) based multicasting technique.
using complex-valued ica to efficiently combine radar polarimetric data for target detection. target detection in sea clutter is a challenging problem in radar detection, specifically, when the doppler return of the target and clutter are collocated. polarization diverse radars provide additional information that enhances target detection. in this paper, we use an effective independent component analysis (ica) approach, adaptive complex maximization of non-gaussianity (a-cmn) [1], to efficiently combine polarimetric radar data prior to detection. we show that a-cmn estimates the polarimetric scatter coefficients for the single target in clutter case, thereby providing matched-filter performance without the need for clutter or target models. the detection performance using ica is evaluated with sea clutter collected with the mcmaster ipix radar off the coast of canada [2]. we also demonstrates the ability of this approach to adapt to the changing sea clutter conditions using simulation results.
iterative filtering of phonetic transcriptions of proper nouns. this paper focuses on an approach to enhancing automatic phonetic transcription of proper nouns by using an iterative filter to retain only the most relevant part of a large set of phonetic variants, obtained by combining rule-based generation with extraction from actual audio signals. using this technique, we were able to reduce the error rate affecting proper nouns during automatic speech transcription of the ester corpus of french broadcast news. the role of the filtering was to ensure that the new phonetic variants of proper nouns would not induce new errors in the transcription of the rest of the words.
detecting sweethearting in retail surveillance videos. a significant portion of retail shrink is attributed to employees and occurs around the point of sale (pos). in this paper, we target a major type of retail fraud in surveillance videos, known as sweethearting (or fake scan), where a cashier intentionally fails to enter one or more items into the transaction in an attempt to get free merchandise for the customer. we first develop a motion-based algorithm to identify video segments as candidates for primitive events at the pos. we then apply spatio-temporal features to recognize true primitive events from the candidates and prune those falsely alarmed. in particular, we learn location-aware event models by multiple-instance learning to address the location-sensitive issues that appear in our problem. finally, we validate the entire transaction by combining primitive events according to temporal ordering constraints. we demonstrate the effectiveness of our approach on data captured from a real grocery store.
field inversion by consensus and compressed sensing. we study the inversion of a random field from pointwise measurements collected by a sensor network. we assume that the field has a sparse representation in a known basis. to illustrate the approach, consider the inversion of an acoustic field created by the superposition of a discrete number of propagating noisy acoustic sources. our method combines compressed sensing (sparse reconstruction by ℓ1-constrained optimization) with distributed average consensus (mixing the pointwise sensor measurements by local communication among the sensors). the paper describes the approach and demonstrates its good performance with synthetic data for several scenarios of practical interest.
combining independent component analysis with geometric information and its application to speech processing. in this paper, we propose two approaches for combining geometric information with ica algorithm to solve permutation problem under the scenario where a rough information about the direction of the desired source is known. the first approach is a new blind extraction algorithm with a soft quadratic geometric constraint. the desired source is guaranteed to be conveyed to the output with little distortion by the quadratic constraint and the negentropy maximization criterion is used to ensure that the other sources get suppressed at the output. the second approach employs a quadratic geometric test as a post-processing step to pickup the desired source after ica processing. an advantage of the proposed two approaches is that they do not require accurate knowledge of the number of sources in the mixtures to recover the desired source, in contrast, other geometric ica approaches usually fail if the number of sources is not known accurately.
an overlap save algorithm for block convolution with reduced complexity. we propose a block convolution algorithm that requires shorter length fft than the conventional overlap save algorithm (osa). it is shown that the osa can be split into two separate processes related to the previous and current data blocks. hence, only current data block needs to be transformed in the proposed osa, whereas the concatenated block of previous and current data is transformed in the conventional method. as a result, the number of arithmetic operations for the block convolution is reduced. also, the reduced transform size gives additional advantage in data manipulation when implemented on dsp and pc.