waashk/dblp huggingface dataset:
energy-constrained discriminant analysis. dimensionality reduction algorithms have become an indispensable tool for working with high-dimensional data in classification. linear discriminant analysis (lda) is a popular analysis technique used to project high-dimensional data into a lower-dimensional space while maximizing class separability. although this technique is widely used in many applications, it suffers from overfitting when the number of training examples is on the same order as the dimension of the original data space. when overfitting occurs, the direction of the lda solution can be dominated by low-energy noise and therefore the solution becomes non-robust to unseen data. in this paper, we propose a novel algorithm, energy-constrained discriminant analysis (ecda), that overcomes the limitations of lda by finding lower dimensional projections that maximize inter-class separability, while also preserving signal energy. our results show that the proposed technique results in higher classification rates when compared to comparable methods. the results are given in terms of sar image classification, however the algorithm is broadly applicable and can be generalized to any classification problem.
multidimensional localization of multiple sound sources using averaged directivity patterns of blind source separation systems. in this paper, we propose a versatile acoustic source localization framework exploiting the self-steering capability of blind source separation (bss) algorithms. we provide a way to produce an acoustical map of the scene by computing the averaged directivity pattern of bss demixing systems. since bss explicitly accounts for multiple sources in its signal propagation model, several simultaneously active sound sources can be located using this method. moreover, the framework is suitable to any microphone array geometry, which allows application for multiple dimensions, in the near field as well as in the far field. experiments demonstrate the efficiency of the proposed scheme in a reverberant environment for the localization of speech sources.
interpolation of head-related transfer functions by spatial linear prediction. head-related transfer functions (hrtfs) are essential for creating a virtual sound source when sound waves are transmitted through headphones. the measurement of hrtfs is a complicated and time-consuming task. therefore, the interpolation of hrtfs is crucial for virtual auditory display systems in which both listeners and sound objects are likely to move in a virtual auditory space. in this study, hrtfs are measured with a high spatial resolution in order to develop an effective interpolation method. an analysis of hrtfs indicates that the functions exhibit periodicity in amplitude along the azimuthal angle. the optimum filter coefficients required to interpolate hrtfs from several functions measured along multiple azimuthal directions are derived. computer simulations indicate that in comparison to conventional methods, the proposed method yields less estimation error in the interpolation of hrtfs. listening tests indicate that the proposed method can provide better perception of virtual sound.
gaussian backend design for open-set language detection. this paper proposes a new approach to the challenging open-set language detection task. most state-of-the-art approaches make use of data sources with several out-of-set languages to model such languages. in the proposed approach, no additional data from out-ofset languages is required, only date from the target languages is used. experiments are conducted using the lre-05 and the lre-07 evaluation data sets with the 30s condition. a cavg of 4.5% and 3.4% is obtained on these data set, respectively. these results are comparable with other reported results.
speaker dependency of spectral features and speech production cues for automatic emotion classification. spectral and excitation features, commonly used in automatic emotion classification systems, parameterise different aspects of the speech signal. this paper groups these features as speech production cues, broad spectral measures and detailed spectral measures and looks at how they differ in their performance in both speaker dependent and speaker independent systems. the extent of speaker normalisation on these features is also considered. combinations of different features are then compared in terms of classification accuracies. evaluations were conducted on the ldc emotional speech corpus for a fiveclass problem. results indicate that mfccs are very discriminative but suffer from speaker variability. further, results suggest that the best front end for a speaker independent system is a combination of pitch, energy and formant information.
adaptive lifting scheme-based method for joint coding 3d-stereo images with luminance correction and optimized prediction. this paper proposes a new adaptive scheme for stereo image coding . the original left and right images are jointly coded in order to exploit the high correlation between the two images for a lossless coding. our goal is to design an efficient transform that reduces the existing redundancy in the stereo pair. this approach is inspired by the lifting scheme (ls). first, illumination change compensation has been made. secondly, the prediction filtering is locally adapted and is based on local horizontal and vertical gradient information. in the sense that small gradient evokes predictability in the same direction. the coefficients of the predictor are then optimized. experimental results show improvement in term of performance and complexity compared to other proposed methods.
robust 3d modeling from silhouette cues. we consider the problem of 3d modeling under the environments where colors of the foreground objects are similar to the background, which poses a difficult problem of foreground and background classification. a purely image-based algorithm is adopted in this paper, with no prior information about the foreground objects. we classify foreground and background by fusing the information at the pixel and region levels to obtain the similarity probability map, followed by a bayesian sensor fusion framework to infer the space occupancy grid. the estimation of the occupancy allows incremental updating once a new observation is available, and the contribution of each observation can be adjusted according to its reliability. finally, three parameters in the algorithm are analyzed in detail and experiments show the effectiveness of this method.
linear receivers for frequency-selective mimo channels with redundant linear precoding can achieve full diversity. since the introduction of the diversity-multiplexing tradeoff (dmt) by zheng and tse for ml reception in frequency-flat mimo channels, some results have been obtained also for the dmt of frequency-selective mimo channels and for the dmt of suboptimal receivers such as linear (les) and decision-feedback equalizers (dfes) for frequency-selective simo channels or frequency-flat mimo channels. we have recently extended these results to the case of linear receivers for frequency-selective mimo channels. however, the diversity properties of linear receivers turn out to be fairly catastrophic. in this paper we show that full diversity can be restored by the introduction of a convolutive linear mimo precoding scheme that we showed earlier to allow to attain the optimal dmt for ml or dfe detection (in the frequency-flat case). the precoder needs to be used with a moderate amount of redundancy in the form of zero-padding, and with a mmse design for the linear equalizer. a mmse-zf design also benefits substantially from the precoding. the proposed scheme is a significant extension of an earlier siso result by tepelenlioglu to the mimo case.
robust discriminative keyword spotting for emotionally colored spontaneous speech using bidirectional lstm networks. in this paper we propose a new technique for robust keyword spotting that uses bidirectional long short-term memory (blstm) recurrent neural nets to incorporate contextual information in speech decoding. our approach overcomes the drawbacks of generative hmm modeling by applying a discriminative learning procedure that non-linearly maps speech features into an abstract vector space. by incorporating the outputs of a blstm network into the speech features, it is able to make use of past and future context for phoneme predictions. the robustness of the approach is evaluated on a keyword spotting task using the humaine sensitive artificial listener (sal) database, which contains accented, spontaneous, and emotionally colored speech. the test is particularly stringent because the system is not trained on the sal database, but only on the timit corpus of read speech. we show that our method prevails over a discriminative keyword spotter without blstm-enhanced feature functions, which in turn has been proven to outperform hmm-based techniques.
distance blurring for space-variant image coding. we present a novel selective blurring algorithm that mimics the optical distance blur effects that occur naturally in cameras and eyes. the proposed algorithm provides a realistic simulation of distance blurring, with the desirable properties of aiming to mimic occlusion effects as occur in natural blurring, and of being able to handle any number of blurring and occlusion levels with the same order of computational complexity. we have performed subjective experiments to compare the perceived quality of distance blurred images with that of foveation-filtered images under equivalent conditions, when both are used as space-variant prefiltering stages prior to a jpeg encoder. the results show that the distance-based blurring was significantly preferable to the foveation blurring for four out of nine test images, whereas a significant converse preference was found for only one test image.
a hierarchical grid feature representation framework for automatic image annotation. we propose a hierarchical-grid (hg) feature analysis framework for representing images in automatic image annotation (aia). we explore the properties of codebooks constructed with different-sized grids in image sub-blocks, and co-occurrence relationship between vq codewords constructed from different grid systems. the proposed hg approach is evaluated on the trecvid 2005 data set using classifiers obtained with maximal figure-of-merit discriminative training. with multi-level and cross-level grid systems incorporating bigram information within and between higher and lower grid levels, we show that the aia performance can be significantly improved. for 20 selected concepts from the 39-concept lscom-lite annotation set, we achieve a best f1 in almost all the concepts. the overall performance improvement with the combined multi-level and cross-level grid systems over the best single-size grid system in micro f1 is about 12.1%.
making chroma features more robust to timbre changes. chroma-based audio features are a well-established tool for analyzing and comparing music data. by identifying spectral components that differ by a musical octave, chroma features show a high degree of invariance to variations in timbre. in this paper, we describe a novel procedure for making chroma features even more robust to changes in timbre and instrumentation while keeping their discriminative power. our idea is based on the generally accepted observation that the lower mel-frequency cepstral coefficients (mfccs) are closely related to timbre. now, instead of keeping the lower coefficients, we will discard them and only keep the upper coefficients. furthermore, using a pitch scale instead of a mel scale allows us to project the remaining coefficients onto the twelve chroma bins. our systematic experiments show that the resulting chroma features have indeed gained a significant boost towards timbre invariance.
a fast method for classifying surface textures. surface texture classification is an important aspect of computer vision and a well studied problem. in this paper, we greatly increase speed for texture classification while maintaining accuracy. we take inspiration form past work and propose a new method for texture classification which is extremely fast due to the low dimensionality of our feature space. we extract distinctive features at a very early stage, thus removing the dependency on expensive and sensitive operations such as k-means clustering which is used by much work in this field of research. we present experimental results on the colombia-utrecht reflectance and texture database (curet), to date the most challenging dataset for texture classification, and show that our method achieves comparable classification accuracy in comparison with the state-of-the-art, but at a 10-fold increased speed.
inverse halftoning with variance classified filtering. inverse halftoning is a key technology to yield a continuous tone image from a halftone image. the main application is to make some image enhancement or further compression of halftones more feasible. many former approaches have been proposed in the literature. among these, a recent method proposed by chung and wu using edge-based lookup table achieves good image quality, where the edge feature is adopted to refine the trained look-up table (lut). however, it has three deficiencies, including 1) the edge features are limited in some predefined cases, which cannot full represent every potential possibility, 2) the lookup table grows exponentially when extreme grayscales are attempted to be recorded, and 3) the trained lookup table cannot fully include all the cases, which leaves some halftone patterns in practice have no associated output grayscale. chang et al.'s method employed one trained filter to compensate the halftone patterns that are not recorded in lut. however, one filter cannot fully characterize the full textures in an image. in this study, the halftone patterns are classified according to its variance and then used to train the corresponding filter sets, which are then employed to provide higher prediction accuracy by inner product with the corresponding halftone patterns. as documented in the experimental results, the proposed inverse halftoning provides excellent performance in image quality and memory consumption than former approaches.
adaptive coding of images via multiresolution ica. multiresolution (mr) representations have been very successful in image encoding, due to both their algorithmic performance and coding efficiency. however these transforms are fixed, suggesting that coding efficiency could be further improved if a multiresolution code could be adapted to a specific signal class. among adaptive coding methods, independent component analysis (ica) provides the best linear code by finding a linear transform with maximally independent coefficients, given a specific signal distribution. this technique, however, scales poorly with the dimensionality of the data, and has been ill-suited for large-scale image coding. we propose a hybrid method (multi-resolution ica) which derives an ica basis for each subband space produced by a given mr transform over the image class. we find that this method produces a significantly more efficient code compared to the mr transform alone. we provide both quantitative and qualitative assessments of coding performance, and illustrate improvement over standard (i.e., non-adaptive) wavelet-based representations such as that used in jpeg2000.
packetized video transmission for ofdm wireless systems with dynamic ordered subcarrier selection algorithm. in this paper, we proposed a dynamic ordered subcarrier selection algorithm (dossa) for ofdm based video transmission system. the proposed scheme is shown to achieve lower bit error rate (ber) than the previously proposed ossa by first selecting a fraction of the subcarriers with highest channel gain. the content information is then exploited in order to extend the ossa to achieve unequal error protection (uep) for packets of different importance. simulation results show that system that utilizes the proposed scheme can achieve higher psnr, especially at low snr, compared to those that use the equal error protection (eep) ossa.
array interpolation based on local polynomial approximation with application to doa estimation using weighted music. the problem of direction-of-arrival (doa) estimation using an array of sensors has received much attention for more than 3 decades. this is due to a rich interest from application areas such as radar, sonar and wireless communication channel characterization. however, high resolution doa estimation requires an accurate model of the array response. this is usually achieved by measuring the response using sources at known positions (calibration). this paper considers interpolation of the calibration measurements using knowledge of a nominal response model. standard linear interpolation is compared to an approach based on local polynomial approximation (lpa). we also derive a weighted music estimator, which is applied using error estimates from the interpolation. both lpa interpolation and weighted music are found to improve the performance, but not uniformly in all scenarios.
phonological features in discriminative classification of dysarthric speech. in an attempt to overcome problems associated with articulatory limitations and generative models, this work considers the use of phonological features in discriminative models for disabled speech. specifically, we train feed-forward and recurrent neural networks, and radial basis and sequence-kernel support vector machines to abstractions of the vocal tract, and apply these models to phone recognition on dysarthric speech. the results show relative error reduction of between 1.5% and 10.9% with this approach against standard hidden markov modeling, and increases in accuracy with speaker intelligibility across all classifiers. this work may be applied within components of assistive software for speakers with dysarthria.
temporal contrast normalization and edge-preserved smoothing on temporal modulation structure for robust speech recognition. in this paper, we propose a two-step processing algorithm which adaptively normalizes the temporal modulation of speech to extract robust speech feature for automatic speech recognition systems. the first step processing is to normalize the temporal modulation contrast (tmc) of the cepstral time series for both clean and noisy speech. the second step processing is to smooth the normalized temporal modulation structure to reduce the artifacts due to noise while preserving the speech modulation events (edges). we tested our algorithm on speech recognition experiments in additive noise condition (aurora-2j data corpus), reverberant noise condition (convolution of clean speech utterances from aurora-2j with a smart room impulse response), and noisy condition with both reverberant and additive noise (air conditioner noise in a smart room). for comparison, the etsi advanced front-end (afe) algorithm was used. our results showed that the algorithm provided: (1) for additive noise condition, 57.26% relative word error reduction (rwer) rate for clean conditional training (59.37% for afe), and 33.52% rwer rate for multi-conditional training (35.77% for afe), (2) for reverberant condition, 51.28% rwer rate (10.17% for afe) and (3) for noisy condition with both reverberant and additive noise, 71.74% rwer rate (48.86% for afe).
degraded image analysis using zernike moment invariants. in real imaging system, the observed image is usually corrupted by blurring, spatial degradations. the classical recognition methods in degraded image analysis are to obtain blur invariants based on geometric moments or complex moments. in this paper, we introduce blur invariants based on zernike moments which are orthogonal over a unit circle. both the expression of zernike moments of blurred image and the set of blur invariants based on zernike moments are presented and proved mathematically. compared with the pattern classification results of complex moments, the experimental results of zernike moment demonstrate that the proposed method performs well in object and pattern recognition.
audio-assisted trajectory estimation in non-overlapping multi-camera networks. we present an algorithm to improve trajectory estimation in networks of non-overlapping cameras using audio measurements. the algorithm fuses audiovisual cues in each camera's field of view and recovers trajectories in unobserved regions using microphones only. audio source localization is performed using stereo audio and cycloptic vision (stac) sensor by estimating the time difference of arrival (tdoa) between microphone pair and then by computing the cross correlation. audio estimates are then smoothed using kalman filtering. the audio-visual fusion is performed using a dynamic weighting strategy. we show that using a multi-modal sensor with combined visual (narrow) and audio (wider) field of view can enable extended target tracking in non-overlapping camera settings. in particular, the weighting scheme improves performance in the overlapping regions. the algorithm is evaluated in several multi-sensor configurations using synthetic data and compared with state of the art algorithm.
progressive lossless compression of medical images. this paper describes a lossless compression method for medical images that produces an embedded bit-stream, allowing progressive lossy-to-lossless decoding with l-infinity oriented rate-distortion. the experimental results show that the proposed technique produces better average lossless compression results than several other compression methods, including jpeg2000, jpeg-ls and jbig, in a publicly available medical image database containing images from several modalities.
scalable superwideband extension for wideband coding. recent trends in speech and audio codec standardization include scalability and extending the signal bandwidth beyond wideband (wb) to superwideband (swb). in this paper we introduce a swb extension for the itu-t g.718 wb codec. in the swb extension the high frequency content is generated utilizing the quantized mdct domain coefficients of the wb core, which enables low additional delay. the proposed implementation is scalable with 4 kbps layers. in the first layer two different coding modes are used depending on the input signal type. the proposed swb extension is evaluated with listening tests and complexity analysis.
extraction of cochlear processed formants for prediction of temporally localized distortions in synthesized speech. temporally localized distortions account for the most variance in subjective evaluation of coded speech signals [1, 2]. the ability to discern and decompose perceptually relevant temporally localized coding noise from other types of distortions is both of theoretical importance as well as a valuable tool for deploying and designing speech synthesis systems. the work described within, uses a physiologically motivated cochlear model to provide a trackable analysis of formant trajectories as processed by the cochlea. subsequent statistical analysis shows simple relationships between the jilter of these trajectories and temporal attributes of the diagnostic acceptability meausre (dam).