Abstract

In the presence of unmodelled dynamics and uncertainties with no a priori constant bounds, conventional robust adaptation strategies for switched systems cannot allow the control gains of inactive subsystems to remain constant during inactive intervals: vanishing gains are typically required in order to prove bounded stability. As a consequence, these strategies, known in literature as leakage-based adaptive methods, might introduce poor transients at each switching instant. Leakage-based adaptive control becomes even more problematic in the switched nonlinear case, where non-conservative state-dependent upper bounds for uncertainties and unmodelled dynamics are required. This work shows that, for a class of switched Euler–Lagrange systems, such difficulties can be mitigated: a novel leakage-based stable mechanism is introduced which allows the gains of inactive subsystems to remain constant. At the same time, unmodelled dynamics and uncertainties with no a priori bounds can be handled by a quadratic state-dependent upper bound structure that reduces conservativeness as compared to state-of-the-art structures. The proposed design is validated analytically and its performance is studied in simulation with a pick-and-place robotic manipulator example.

Abstract

An index code for broadcast channel with receiver side information is locally decodable if each receiver can decode its demand by observing only a subset of the transmitted codeword symbols instead of the entire codeword. Local decodability in index coding is known to reduce receiver complexity, improve user privacy and decrease decoding error probability in wireless fading channels. Conventional index coding solutions assume that the receivers observe the entire codeword, and as a result, for these codes the number of codeword symbols queried by a user per decoded message symbol, which we refer to as locality, could be large. In this paper, we pose the index coding problem as that of minimizing the broadcast rate for a given value of locality (or vice versa) and designing codes that achieve the optimal trade-off between locality and rate. We identify the optimal broadcast rate corresponding to the minimum possible value of locality for all single unicast problems. We present new structural properties of index codes which allow us to characterize the optimal trade-off achieved by: vector linear codes when the side information graph is a directed cycle; and scalar linear codes when the minrank of the side information graph is one less than the order of the problem. We also identify the optimal trade-off among all codes, including non-linear codes, when the side information graph is a directed 3-cycle. Finally, we present techniques to design locally decodable index codes for arbitrary single unicast problems and arbitrary values of locality. Index Terms— Broadcast rate, directed cycle, index coding, linear codes, locally decodable codes, minrank.

Abstract

We propose Domain Decomposed Bundle Adjustment (DDBA), a robust and efficient solver for the bundle adjustment problem. Bundle adjustment (BA) is generally formulated as a non-linear least squares problem and is solved by some variant of the Levenberg-Marquardt (LM) algorithm. Each iteration of the LM algorithm requires solving a system of normal equations, which becomes computationally expensive with the increase in problem size. The coefficient matrix of this system has a sparse structure which can be exploited for simplifying the computations in this step. We propose a technique for approximating the Schur complement of the matrix, and use this approximation to construct a preconditioner, that can be used with the Generalized Minimal Residual (GMRES) algorithm for solving the system of equations. Our experiments on the BAL dataset show that the proposed method for solving the system is faster than GMRES solve preconditioned with block Jacobi and more memory efficient than direct solve.

Abstract

A fundamental essence for emotional speech analysis towards emotion recognition is a good database. Database collected from natural scenarios consists of spontaneous emotions, but there are several issues in collection of such database. Other than the privacy and legal related concerns, there is no control over environment at the background. As it is difficult to collect data from natural scenarios, many research groups have collected data from semi-natural or designed procedures. In this paper, a new emotional speech database named IIIT-H TEMD (International Institute of Information Technology-Hyderabad Telugu Emotional Database) is collected using designed drama situations from actors and non-actors. Utterances are manually annotated using a hybrid strategy by giving the context to one of the listeners. As some of the data collection studies in the literature recommend for actors, analysis of actors data versus non-actors data is carried out for their significance. The total size of the database is about 5 hours, which makes it an useful resource for the emotional speech analysis.

Abstract

Parkinson’s disease (PD) is a progressive deterioration of the human central nervous system. Detection of PD (discriminating patients with PD from healthy subjects) from speech is a useful approach due to its non-invasive nature. This study proposes to use novel cepstral coefficients derived from the single frequency filtering (SFF) method, called as single frequency filtering cepstral coefficients (SFFCCs) for the detection of PD. SFF has been shown to provide higher spectro-temporal resolution compared to the short-time Fourier transform. The current study uses the PC-GITA database, which consists of speech from speakers with PD and healthy controls (50 males, 50 females). Our proposed detection system is based on the i-vectors derived from SFFCCs using SVM as a classifier. In the detection of PD, better performance was achieved when the ivectors were computed from the proposed SFFCCs compared to the popular conventional MFCCs. Furthermore, we investigated the effect of temporal variations by deriving the shifted delta cepstral (SDC) coefficients using SFFCCs. These experiments revealed that the i-vectors derived from the proposed SFFCCs+SDC features gave an absolute improvement of 9% compared to the i-vectors derived from the baseline MFCCs+SDC features, indicating the importance of temporal variations in the

Abstract

Automatic detection and assessment of voice disorders is important in diagnosis and treatment planning of voice disorders. This work proposes an approach for automatic detection and assessment of voice disorders from a clinical perspective. To accomplish this, a multi-level classification approach was explored in which four binary classifiers were used for the assessment of voice disorders. The binary classifiers were trained using support vector machines with excitation source features, vocal-tract system features, and state-of-art OpenSMILE features. In this study source features namely, glottal parameters obtained from glottal flow waveform, perturbation measures obtained from epoch locations, and cepstral features obtained from linear prediction residual and zero frequency filtered signal were explored. The present study used the Saarbucken voice disorders database to evaluate the performance of proposed approach. The OpenSMILE features namely ComParE and eGEMAPS feature sets shown better performance in terms of classification accuracies of 82.8% and 76%, respectively for voice disorder detection. The combination of excitation source features with baseline feature sets further improved the performance of detection and assessment systems, that highlight the complimentary nature of exciting source features.

Abstract

Formants are resonances of the time varying vocal tract system, and their characteristics are reflected in the response of the system for a sequence of impulse-like excitation sequence originated at the glottis. This paper presents a method to enhance the formants information in the display of spectrogram of the speech signal, especially for high pitched voices. It is well known that in the narrowband spectrogram, the presence of pitch harmonics masks the formant information, whereas in the wideband spectrogram, the formant regions are smeared. Using single frequency filtering (SFF) analysis, we show that the wideband equivalent SFF spectrogram can be modified to enhance the formant information in the display by improving the frequency resolution. For this, we obtain two SFF spectrograms by using single frequency filtering of the speech signal at two closely spaced roots on the real axis in the z-plane. The ratio or difference of the two SFF spectrograms is processed to enhance the formant information in the spectrographic display. This will help in tracking rapidly changing formants and in resolving closely spaced formants. The effect is more pronounced in the case of high-pitched voices, like female and children speech. Keywords: formants, spectrograms, single frequency filtering

Abstract

Twisting of a building about vertical axis increases the shear force demand on lateral force resisting elements and it is not desirable as increased shear force results in brittle failure. Generally, twisting is induced in a building due to eccentricity between centre of Mass (CM) and centre of Stiffness (CS) at diaghragm level. Eccentricity can be induced in a structure due to mass, strenght and stiffness arising out of various construction and design limitations. Code insists to apply design forces calculated according to equivalent static method or response spectrum method at displaced center of Mass (CM) so as to cause design eccentricity between CM and CS. The displaced center of mass resulting from design eccentricity consists of two terms static eccentricity and accidental eccentricity. Also, earthquake ground motion has the ability to induce torsion in a structure. Hence in order to account for all the eccentricities, code suggests 5% accidental eccentricity to be included in design eccentricity. In current work, an adjustment of mass eccentricity is verified. For this purpose, three one storey models are created with 5 % unidirectional mass eccentricity (CM), 5% unidirectional stiffness eccentricity (CS) and 5% unidirectional Mass-Stiffness eccentricity (CM-CS). Linear Increment Dynamic Analysis is performed by considering chamoli earthquake and edge displacements are obtained. Later, twist is calculated based on edge displacements. It is concluded that the same amount of mass and stiffness eccentricity (i.,e 5%) , the twist incurred in stiffness eccentric model is signifcantly higher whereas no twist about vertical axis is noticed in mass eccentric model unless mass eccentricity has significant impact on torsional response, the torsional response of combined CM-CS eccentric model is approximately equal to CS eccentric model. The results conclude that universal adjustment of center of mass will not provide the assumed conservativeness of safety against torsion. Apart from this, few countries guidelines like New Zealand [NZS, 2004] has revised accidental eccentricity from 5% to 10%. Hence, design eccentricity needs revision considering the statistical evaluation of various paramters inducing accidental eccentricities , without adjusting mass centre for every eccentricity.

Abstract

This paper describes Naver Labs Europe’s participation in the Robustness, Chat, and Biomedical Translation tasks at WMT 2020. We propose a bidirectional German ↔ English model that is multi-domain, robust to noise, and which can translate entire documents (or bilingual dialogues) at once. We use the same ensemble of such models as our primary submission to all three tasks and achieve competitive results. We also experiment with language model pre-training techniques and evaluate their impact on robustness to noise and out-of-domain translation. For German, Spanish, Italian, and French to English translation in the Biomedical Task, we also submit our recently released multilingual Covid19NMT model.

Abstract

Word segmentation is a fundamental task for most of the NLP applications. Urdu adopts Nastalique writing style which does not have a concept of space. Furthermore, the inherent non-joining attributes of certain characters in Urdu create spaces within a word while writing in digital format. Thus, Urdu not only has space omission but also space insertion issues which make the word segmentation task challenging. In this paper, we improve upon the results of Zia, Raza and Athar (2018) by using a manually annotated corpus of 19,651 sentences along with morphological context features. Using the Conditional Random Field sequence modeler, our model achieves F1 score of 0.98 for word boundary identification and 0.92 for sub-word boundary identification tasks. The results demonstrated in this paper outperform the state-of- the-art methods.