waashk/dblp huggingface dataset:
query parsing for voice-enabled mobile local search. with the exponential growth in the number of mobile devices, voice enabled local search is emerging as one of the most popular applications. although the application is essentially an integration of automatic speech recognition (asr) and text or database search, the potential usefulness of this application has been widely acknowledged. in this paper, we present a data-driven approach to voice query parsing, that segments the input query into several fields that are necessary for high-precision local search. we also demonstrate the robustness of our approach to asr errors. we present an approach for exploiting multiple hypotheses from asr, in the form of word confusion networks, in order to achieve tighter coupling between asr and query parsing. a confusion-network based query parsing outperforms asr 1-best based query-parsing by 2.6% absolute.
iris recognition using 2d-lda + 2d-pca. this paper presents a biometric iris recognition using 2d-lda with embedding 2d-pca. a new approach that the 2d-pca is embedded into the 2d-lda to improve its performance is proposed. the approach first finds the most concentrated training samples in each class, and uses the sample mean to represent the class. then the 2d-pca is adopted to find the projection matrix which can scatter the variance between classes. the results show that the new approach has an encouraging performance. the recognition rate up to 99.20% can be achieved.
a tempering approach for itakura-saito non-negative matrix factorization. with application to music transcription. in this paper we are interested in non-negative matrix factorization (nmf) with the itakura-saito (is) divergence. previous work has demonstrated the relevance of this cost function for the decomposition of audio power spectrograms. this is in particular due to its scale invariance, which makes it more robust to the wide dynamics of audio, a property which is not shared by other popular costs such as the euclidean distance or the generalized kulback-leibler (kl) divergence. however, while the latter two cost functions are convex, the is divergence is not, which makes it more prone to convergence to irrelevant local minima, as observed empirically. thus, the aim of this paper is to propose a tempering scheme that favors convergence of is-nmf to global minima. our algorithm is based on nmf with the beta-divergence, where the shape parameter beta acts as a temperature parameter. results on both synthetical and music data (in a transcription context) show the relevance of our approach.
block jacobi-type methods for non-orthogonal joint diagonalisation. in this paper, we study the problem of non-orthogonal joint diagonalisation of a set of real symmetric matrices via simultaneous conjugation. a family of block jacobi-type methods are proposed to optimise two popular cost functions for the non-orthogonal joint diagonalisation, namely, the off-norm function and the log-likelihood function. by exploiting the appropriate underlying manifold, namely the so-called oblique manifold, rigorous analysis shows that, under the exact non-orthogonal joint diagonalisation setting, the proposed methods converge locally quadratically fast to a joint diagonaliser. finally, performance of our methods is investigated by numerical experiments for both exact and approximate non-orthogonal joint diagonalisation.
rate-distortion optimized bitstream extractor for motion scalability in scalable video coding. motion scalability is designed to improve the coding efficiency of a scalable video coding framework, especially in the medium to low range of decoding bit rates or spatial resolutions. in order to fully benefit from the superiority of motion scalability, a rate-distortion optimized bitstream extractor, which determines the optimal motion quality layer for each decoding scenario, is required. in this paper, the determination process first starts off with a brute force searching algorithm. although guaranteed by the optimal performance within the search domain, it has high computational complexity. two properties, i.e. the monotonically non-decreasing property and the unimodal property, are then derived to accurately describe the rate-distortion behavior of motion scalability. based on these two properties, modified searching algorithms are proposed to reduce the complexity by a factor up to 5.
vlsi for 5000-word continuous speech recognition. we have developed a vlsi chip for 5,000 word speaker-independent continuous speech recognition. this chip employs a context-dependent hmm (hidden markov model) based speech recognition algorithm, and contains emission probability and viterbi beam search pipelined hardware units. the feature vector for speech recognition is computed using a host processor in software in order to adopt various enhancement algorithms. the amount of internal sram size is minimized by moving data out to the external dram, and a custom dram controller module is designed to efficiently read and write consecutive data. the experimental result shows that the implemented system has a real-time factor of 0.77 and 0.55 using sdram and ddr sdram, respectively.
improved video segmentation through robust statistics and mpeg-7 features. video segmentation is an important task for a wide range of applications like content-based video coding or video retrieval. in this paper, a new spatio-temporal video segmentation framework is presented. it is based upon robust statistics, namely an m-estimator, and incorporates an mpeg-7 descriptor for consistent temporal labeling of identified textures. the algorithm is based on assumptions about the geometric modifications a given moving region undergoes with time as well as on its surface properties. homogeneously moving segments are described using a parametric motion scheme. the latter is used to piecewise fit the optical flow field in order to extract rigid motion areas. robust statistics are used to carefully constrain split, merge and contour refinement decisions. experimental results show that regions detected by the proposed method are more reliable than the state-of-the-art. true region boundaries are moreover better detected.
quality control of automatic labelling using hmm-based synthesis. this paper presents a measure to verify the quality of automatically aligned phone labels. the measure is based on a similarity cost between automatically generated phonetic segments and phonetic segments generated by an hmm-based synthesiser. we investigate the effectiveness of the measure for identifying problems of three types: alignment errors, phone identity problems and noise insertion. our experiments show that the measure is best at finding noise errors, followed by phone identity mismatches and serious misalignments.
blind sparse source separation for unknown number of sources using gaussian mixture model fitting with dirichlet prior. in this paper, we propose a novel sparse source separation method that can be applied even if the number of sources is unknown. recently, many sparse source separation approaches with time-frequency masks have been proposed. however, most of these approaches require information on the number of sources in advance. in our proposed method, we model the histogram of the estimated direction of arrival (doa) with a gaussian mixture model (gmm) with a dirichlet prior. then we estimate the model parameters by using the maximum a posteriori estimation based on the em algorithm. in order to avoid one cluster being modeled by two or more gaussians, we utilize a sparse distribution modeled by the dirichlet distributions as the prior of the gmm mixture weight. by using this prior, without any specific model selection process, our proposed method can estimate the number of sources and time-frequency masks simultaneously. experimental results show the performance of our proposed method.
comparison of frequency domain noise reduction strategies based on multichannel wiener filtering and spatial prediction. in this paper two multichannel noise reduction strategies are compared in the context of binaural hearing aids. recently a novel noise reduction method based on spatial-temporal prediction (stp) was introduced which showed an improvement over methods based on multichannel wiener filtering, although at the cost of a higher computational complexity. whereas this newmethod operates in the time domain, hearing aids typically demand faster frequency domain implementations. in this paper we therefore propose a frequency domain equivalent of the stp method. the performance of the new so-called spatial prediction (sp) method will be compared to a frequency domain implementation of the speech distortion weighted multichannel wiener filter (sdw-mwf), theoretically as well as based on simulations with a binaural hearing aid configuration. it will be shown that the frequency domain sp method still achieves some improvement over the sdw-mwf, at the cost of higher computational complexity.
non-linear mapping for multi-channel speech separation and robust overlapping spech recognition. this paper investigates a non-linear mapping approach to extract robust features for asr and speech separation of overlapping speech. based on our previous studies, we continue to use two additional sound sources, namely from the target and interfering speakers. the focuses of this work are: 1) we investigate the feature mapping between different domains with the consideration of mmse criterion and regression optimizations, demonstrating the mapping of log melfilterbank energies to mfcc can be exploited to improve the effectiveness of the regression; 2) we investigate the data-driven filtering for the speech separation by using the mapping method, which can be viewed as a generalized log spectral subtraction and results in better separation performance. we demonstrate the effectiveness of the proposed approach through extensive evaluations on the monc corpus, which includes both non-overlapping single speaker and overlapping multi-speaker conditions.
estimating side-information for wyner-ziv video coding using resolution-progressive decoding and extensive motion exploration. in wyner-ziv video coding (wzvc), the quality of side information (si) has a critical impact on the coding efficiency. most existing wzvc schemes generate si using decoder-side motion estimation (me), where the current frame is unavailable and the me accuracy is greatly impaired. in this paper, without introducing additional bitrate overhead, we incorporate fractional-pel motion search, reduced block sizes and multiple hypothesis prediction into our previously proposed decoder-side multi-resolution motion refinement framework [1], where the current frame is progressively decoded, and based on which the decoder iteratively refines the motion to improve its accuracy. theoretical analysis shows significant gain of the combination of these advanced techniques. a practical si estimator is implemented and provides prediction performance comparable to h.264/avc.
a variational em algorithm for learning eigenvoice parameters in mixed signals. we derive an efficient learning algorithm for model-based source separation for use on single channel speech mixtures where the precise source characteristics are not known a priori. the sources are modeled using factor-analyzed hidden markov models (hmm) where source specific characteristics are captured by an “eigenvoice” speaker subspace model. the proposed algorithm is able to learn adaptation parameters for two speech sources when only a mixture of signals is observed. we evaluate the algorithm on the 2006 speech separation challenge data set and show that it is significantly faster than our earlier system at a small cost in terms of performance.
dynamic texture models of music. in this paper, we consider representing a musical signal as a dynamic texture, a model for both the timbral and rhythmical qualities of sound. we apply the new representation to the task of automatic song segmentation. in particular, we cluster sequences of audio feature-vectors, extracted from the song, using a dynamic texture mixture model (dtm). we show that the dtm model can both detect transition boundaries and accurately cluster coherent segments. the similarities between the dynamic textures which define these segments are based on both timbral and rhythmic qualities of the music, indicating that the dtm model simultaneously captures two of the important aspects required for automatic music analysis.
non-uniform sampling: a novel approach. in this paper a novel approach to non-uniform sampling is proposed. two engineering methods are discussed.
distributed compressive video sensing. low-complexity video encoding has been applicable to several emerging applications. recently, distributed video coding (dvc) has been proposed to reduce encoding complexity to the order of that for still image encoding. in addition, compressive sensing (cs) has been applicable to directly capture compressed image data efficiently. in this paper, by integrating the respective characteristics of dvc and cs, a distributed compressive video sensing (dcvs) framework is proposed to simultaneously capture and compress video data, where almost all computation burdens can be shifted to the decoder, resulting in a very low-complexity encoder. at the decoder, compressed video can be efficiently reconstructed using the modified gpsr (gradient projection for sparse reconstruction) algorithm. with the assistance of the proposed initialization and stopping criteria for grsr, derived from statistical dependencies among successive video frames, our modified gpsr algorithm can terminate faster and reconstruct better video quality. the performance of our dcvs method is demonstrated via simulations to outperform three known cs reconstruction algorithms.
humming-based human verification and identification. this paper considers humming-based systems for human verification and identification. humming of a target person is modeled as a gaussian mixture model, and the matching score between a target model and humming is computed as the likelihood of humming given a target model. verification is performed by comparing the matching score to the likelihood given a universal background model, and identification is performed by selecting the best-matched model. the verification and identification performances are evaluated using various acoustical features. the experimental results show that linear prediction cepstral coefficients and perceptually linear prediction coefficients are conducive to verification and identification, respectively.
adaptive tracking in the time-frequency plane and its application in causal real-time speech analysis. this paper proposes a causal approach to adaptive estimation of time-frequency localized signals using adaptive notch filters (anf). by adaptively estimating the envelope of each sinusoidal component, it is possible to specify the tracking quality and restart the anf unit whenever the tracked sinusoid disappears, as well as preventing the anf from “tracking” non-existent sinusoids (the frequency mis-lock situation). employing multiple anfs allows an efficient approach to tracking time-frequency localized signals such as speech.
a mixed time-scale algorithm for distributed parameter estimation : nonlinear observation models and imperfect communication. the paper considers the algorithm nlu for distributed (vector) parameter estimation in sensor networks, where, the local observation models are nonlinear, and inter-sensor communication is imperfect, in the sense, that the network links fail randomly and inter-sensor transmission is quantized. the paper introduces the class of separably estimable observation models, which generalizes the notion of observability in centralized linear estimation to distributed nonlinear estimation. we show that the nlu algorithm leads to consistent and asymptotically unbiased estimates of the parameter at each sensor for separably estimable observation models. in other words, the sensors reach consensus almost sure (a.s.) to the true parameter value. the algorithm nlu is a mixed time scale stochastic algorithm, characterized by two different decreasing weight sequences associated with the consensus and innovation updates. the analysis of the nlu algorithm, thus, does not follow under the purview of standard stochastic approximation, making the analysis developed in the paper of independent theoretical interest.
reverse channel training for reciprocal mimo systems with spatial multiplexing. this paper investigates the problem of designing reverse channel training sequences for a tdd-mimo spatial-multiplexing system. assuming perfect channel state information at the receiver and spatial multiplexing at the transmitter with equal power allocation to the m dominant modes of the estimated channel, the pilot is designed to ensure an estimate of the channel which improves the forward link capacity. using perturbation techniques, a lower bound on the forward link capacity is derived with respect to which the training sequence is optimized. thus, the reverse channel training sequence makes use of the channel knowledge at the receiver. the performance of orthogonal training sequence with mmse estimation at the transmitter and the proposed training sequence are compared. simulation results show a significant improvement in performance.
generic invertibility of multidimensional fir multirate systems and filter banks. we study the invertibility of m-variate polynomial (respectively : laurent polynomial) matrices of size n by p. such matrices represent multidimensional systems in various settings including filter banks, multiple-input multiple-output systems, and multirate systems. the main result of this paper is to prove that when n − p ≥ m, then h(z) is generically invertible; whereas when n − p ≪ m, then h(z) is generically noninvertible. as a result, we can have an alternative approach in design of the multidimensional systems.
receiver error concealment using acknowledge preview (recap) - an approach to resilient video streaming. high-quality and low-latency video streaming is essential to providing a natural user experience in video conferencing. this is challenging over lossy networks since compressed video is highly fragile while the low-latency requirement limits the effectiveness of traditional error control approaches such as retransmission and forward error correction. in this paper, we advocate a practical solution for low-latency video communications over best-effort networks that employs an additional low-quality, low-resolution but robustly coded copy of the video. this approach, called recap, incurs minimal rate overhead, and can be combined with previously decoded frames to achieve effective concealment of isolated and burst losses even under tight delay constraints. recap achieves psnr gains of 2–6 db against complete frame loss.
robust implementation of the music algorithm. the problem of estimating frequencies of sinusoids in noise has been studied intensively by the signal processing community during the last decades. traditionally high resolution subspace-based techniques suffer from high computational complexity, and generally sensitive to the colored noise. we present here a frequency-domain based subspace parameter estimation algorithm termed frequency-selective multiple signal classification (f-music) that is based on the signal and noise subspace orthogonality property. the method is computationally efficient in providing estimates in the selected subband compared to the classic music. the performance of f-music is evaluated and compared to both music and cramér-rao lower bound (crlb). in a low signal to noise ratio (snr) with colored noise scenarios, f-music outperforms music.
blind detection of high rate orthogonal space-time block codes. we present a new approach to blind equalization for generalized orthogonal space-time block codes. our method takes the form of linear programming (lp) and is globally convergent. we exploit the implicit structure of orthogonal space-time block codes to cast the problem as linear programming that can be solved efficiently. unlike several known methods, the proposed technique is applicable to many full-rate orthogonal space time codes such as the popular almouti code. our algorithm allows receiver detection of full diversity codes without channel knowledge with detection performance comparable to the optimum maximum-likelihood (ml) detection.
online speaker clustering using incremental learning of an ergodic hidden markov model. a novel online speaker clustering method suitable for real-time applications is proposed. using an ergodic hidden markov model, it employs incremental learning based on a variational bayesian framework and provides probabilistic (non-deterministic) decisions for each input utterance, directly considering the specific history of preceding utterances. it makes possible more robust cluster estimation and precise classification of utterances than do conventional online methods. experiments on meeting-speech data show that the proposed method produces 70–80% fewer errors than a conventional method does.
cosine - a corpus of multi-party conversational speech in noisy environments. we present an overview of the data collection and transcription efforts for the conversational speech in noisy environments (cosine) corpus. the corpus is a set of multi-party conversations recorded in real world environments with background noise that can be used to train noise-robust speech recognition systems. we explain the motivation for creating such a corpus and describe the resulting audio recordings and transcriptions that comprise the corpus. these recordings include a 4-channel array and close-talking, far-field, and throat microphones on separate synchronized channels, allowing for unique algorithm research.
a localized vibration response technique for damage detection in bridges. traditional techniques for damage detection in civil infrastructures that are based on the global vibration response of the system are limited in their capabilities to detect damage. because damage detection based on the global vibration response relies on global parameters to describe the dynamic behavior of local structural elements, it suffers from limiting factors such as poorly-formed aggregate system models, very low signal to noise ratio, unrealistic boundary conditions. an alternative to the use of global detection techniques is the use of an adaptive local analysis technique based on the local response of the structure. this is achieved using wavelet packet decomposition at the sensor outputs and interpreting this decomposition as a subband framework for a bank of adaptive beamformers. the beamformer adaptive processors guaranties the maximization of the output snr and in the same time allows for spatial selectivity and a highly directive vibration response in the structure. scanning the structure over time produces a detailed vibration signature of the structure. the structural damage can be localized with high probability by comparing two vibration signatures before and after the damage occurs.
perceptually motivated quasi-periodic signal selection for polyphonic music transcription. a multiple fundamental frequency estimator is a key building block in music transcription and indexing operations. however, systems trying to perform this task tend to be very complex [1]. indeed, music transcription requires an analysis accounting for both physical and psycho-acoustical matters. in this work, we propose a physically-motivated audio signal analysis followed by an auditory-based selection. the audio signal model allows for a better time/frequency resolution tradeoff, while the auditory distance discards the redundant/non-relevant information. no prior information on the musical instrument, musical genre, and/or maximum polyphony are needed. simulations show that the proposed technique achieves good transcription results for a variety of string and wind instruments. the proposed scheme is also shown to be robust in the presence of noise, percussive sounds and in unbalanced signal-to-interference ratio (sir) situations.