Discriminative Minimum Statistics Projection Coefficient Feature for Acoustic Context Recognition

Acoustic environment recognition, which can provide the important acoustic context, has been widely used in many applications and is a considerable difficult problem in the real-life and the complex environment. This paper proposes the discriminative minimum statistics project coefficient (MSPC) feature with the information of classification by using partial least squares (PLS). With the minimum statistics (MS) tracked from the input sound, the discriminative MSPC feature is extracted by projecting the MS into lower-dimensional feature subspace learned by using PLS analysis. Based on the proposed discriminative MSPC feature, the acoustic environment recognition is implemented by using Gaussian Mixture Model (GMM) for modeling each sound class. The experimental results show that the proposed discriminative MSPC feature based on PLS outperforms the MSPC feature based on PCA for acoustic environment recognition.

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Robust minimum statistics project coefficients feature for acoustic environment recognition

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ◽

10.1109/icassp.2014.6855206 ◽

2014 ◽

Cited By ~ 12

Author(s):

Shiwen Deng ◽

Jiqing Han ◽

Chaozhu Zhang ◽

Tieran Zheng ◽

Guibin Zheng

Keyword(s):

Acoustic Environment ◽

Environment Recognition ◽

Minimum Statistics

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Black-Box-Based Mathematical Modelling of Machine Intelligence Measuring

Mathematics ◽

10.3390/math9060681 ◽

2021 ◽

Vol 9 (6) ◽

pp. 681

Author(s):

László Barna Iantovics

Keyword(s):

Problem Solving ◽

Scientific Literature ◽

Real Life ◽

Intelligent Agent ◽

Difficult Problem ◽

Black Box ◽

Machine Intelligence ◽

Agent Based ◽

Np Hard Problem ◽

Box Method

Current machine intelligence metrics rely on a different philosophy, hindering their effective comparison. There is no standardization of what is machine intelligence and what should be measured to quantify it. In this study, we investigate the measurement of intelligence from the viewpoint of real-life difficult-problem-solving abilities, and we highlight the importance of being able to make accurate and robust comparisons between multiple cooperative multiagent systems (CMASs) using a novel metric. A recent metric presented in the scientific literature, called MetrIntPair, is capable of comparing the intelligence of only two CMASs at an application. In this paper, we propose a generalization of that metric called MetrIntPairII. MetrIntPairII is based on pairwise problem-solving intelligence comparisons (for the same problem, the problem-solving intelligence of the studied CMASs is evaluated experimentally in pairs). The pairwise intelligence comparison is proposed to decrease the necessary number of experimental intelligence measurements. MetrIntPairII has the same properties as MetrIntPair, with the main advantage that it can be applied to any number of CMASs conserving the accuracy of the comparison, while it exhibits enhanced robustness. An important property of the proposed metric is the universality, as it can be applied as a black-box method to intelligent agent-based systems (IABSs) generally, not depending on the aspect of IABS architecture. To demonstrate the effectiveness of the MetrIntPairII metric, we provide a representative experimental study, comparing the intelligence of several CMASs composed of agents specialized in solving an NP-hard problem.

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Changes in the Soundscape of the Public Space Close to a Highway by a Noise Control Intervention

Sustainability ◽

10.3390/su13095284 ◽

2021 ◽

Vol 13 (9) ◽

pp. 5284

Author(s):

Timothy Van Renterghem ◽

Francesco Aletta ◽

Dick Botteldooren

Keyword(s):

Public Space ◽

Real Life ◽

Noise Abatement ◽

Acoustic Environment ◽

The Public ◽

Control Intervention ◽

Before And After ◽

Noise Shielding ◽

Visual Preferences ◽

The Impact

The deployment of measures to mitigate sound during propagation outdoors is most often a compromise between the acoustic design, practical limitations, and visual preferences regarding the landscape. The current study of a raised berm next to a highway shows a number of common issues like the impact of the limited length of the noise shielding device, initially non-dominant sounds becoming noticeable, local drops in efficiency when the barrier is not fully continuous, and overall limited abatement efficiencies. Detailed assessments of both the objective and subjective effect of the intervention, both before and after the intervention was deployed, using the same methodology, showed that especially the more noise sensitive persons benefit from the noise abatement. Reducing the highest exposure levels did not result anymore in a different perception compared to more noise insensitive persons. People do react to spatial variation in exposure and abatement efficiency. Although level reductions might not be excessive in many real-life complex multi-source situations, they do improve the perception of the acoustic environment in the public space.

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Feature Subspace Estimation for Resolving Dimensionality Mismatch in Practical Color-Based Face Recognition Systems

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001416560048 ◽

2016 ◽

Vol 30 (04) ◽

pp. 1656004 ◽

Cited By ~ 1

Author(s):

Jae Young Choi

Keyword(s):

Face Recognition ◽

Color Image ◽

Recognition Performance ◽

Real Life ◽

Practical Applications ◽

Novel Approach ◽

Feature Subspace ◽

New Feature ◽

Different Color ◽

Input Level

Recently, considerable research efforts have been devoted to effective utilization of facial color information for improved recognition performance. Of all color-based face recognition (FR) methods, the most widely used approach is a color FR method using input-level fusion. In this method, augmented input vectors of the color images are first generated by concatenating different color components (including both luminance and chrominance information) by column order at the input level and feature subspace is then trained with a set of augmented input vectors. However, in practical applications, a testing image could be captured as a grayscale image, rather than as a color image, mainly caused by different, heterogeneous image acquisition environment. A grayscale testing image causes so-called dimensionality mismatch between the trained feature subspace and testing input vector. Disparity in dimensionality negatively impacts the reliable FR performance and even imposes a significant restriction on carrying out FR operations in practical color FR systems. To resolve the dimensionality mismatch, we propose a novel approach to estimate new feature subspace, suitable for recognizing a grayscale testing image. In particular, new feature subspace is estimated from a given feature subspace created using color training images. The effectiveness of proposed solution has been successfully tested on four public face databases (DBs) such as CMU, FERET, XM2VTSDB, and ORL DBs. Extensive and comparative experiments showed that the proposed solution works well for resolving dimensionality mismatch of importance in real-life color FR systems.

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Gabor-oriented local order feature-based deep learning for face annotation

International Journal of Wavelets Multiresolution and Information Processing ◽

10.1142/s0219691319500322 ◽

2019 ◽

Vol 17 (05) ◽

pp. 1950032

Author(s):

A. Kasthuri ◽

A. Suruliandi ◽

S. P. Raja

Keyword(s):

Semantic Network ◽

Real Life ◽

Texture Feature ◽

Texture Features ◽

Feature Representation ◽

Local Order ◽

Face Images ◽

Novel Approach ◽

The Face ◽

Feature Based

Face annotation, a modern research topic in the area of image processing, has useful real-life applications. It is a really difficult task to annotate the correct names of people to the corresponding faces because of the variations in facial appearance. Hence, there still is a need for a robust feature to improve the performance of the face annotation process. In this work, a novel approach called the Deep Gabor-Oriented Local Order Features (DGOLOF) for feature representation has been proposed, which extracts deep texture features from face images. Seven recently proposed face annotation methods are considered to evaluate the proposed deep texture feature under uncontrolled situations like occlusion, expression changes, illumination and pose variations. Experimental results on the LFW, IMFDB, Yahoo and PubFig databases show that the proposed deep texture feature provides efficient results with the Name Semantic Network (NSN)-based face annotation. Moreover, it is observed that the proposed deep texture feature improves the performance of face annotation, regardless of all the challenges involved.

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Computation of Midsurface by Feature-Based Simplification–Abstraction–Decomposition

Journal of Computing and Information Science in Engineering ◽

10.1115/1.4034130 ◽

2016 ◽

Vol 17 (1) ◽

Cited By ~ 2

Author(s):

Yogesh H. Kulkarni ◽

Anil Sahasrabudhe ◽

Mukund Kale

Keyword(s):

Computer Aided Design ◽

Real Life ◽

Boundary Representation ◽

Manual Task ◽

Computer Aided ◽

Cad Models ◽

Feature Based ◽

Feature Information ◽

Plastic Parts ◽

Aided Design

Computer-aided design (CAD) models of thin-walled solids such as sheet metal or plastic parts are often reduced dimensionally to their corresponding midsurfaces for quicker and fairly accurate results of computer-aided engineering (CAE) analysis. Computation of the midsurface is still a time-consuming and mostly, a manual task due to lack of robust and automated techniques. Most of the existing techniques work on the final shape (typically in the form of boundary representation, B-rep). Complex B-reps make it hard to detect subshapes for which the midsurface patches are computed and joined, forcing usage of hard-coded heuristic rules, developed on a case-by-case basis. Midsurface failures manifest in the form of gaps, overlaps, nonmimicking input model, etc., which can take hours or even days to correct. The research presented here proposes to address these problems by leveraging feature-information available in the modern CAD models, and by effectively using techniques like simplification, abstraction, and decomposition. In the proposed approach, first, the irrelevant features are identified and removed from the input FbCAD model to compute its simplified gross shape. Remaining features then undergo abstraction to transform into their corresponding generic Loft-equivalents, each having a profile and a guide curve. The model is then decomposed into cellular bodies and a graph is populated, with cellular bodies at the nodes and fully overlapping-surface-interfaces at the edges. The nodes are classified into midsurface-patch generating nodes (called “solid cells” or sCells) and interaction-resolving nodes (“interface cells” or iCells). In a sCell, a midsurface patch is generated either by offset or by sweeping the midcurve of the owner-Loft-feature's profile along with its guide curve. Midsurface patches are then connected in the iCells in a generic manner, thus resulting in a well-connected midsurface with minimum failures. Output midsurface is then validated topologically for correctness. At the end of this paper, real-life parts are used to demonstrate the efficacy of the proposed approach.

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Tomographic Feature-Based Map Merging for Multi-Robot Systems

Electronics ◽

10.3390/electronics9010107 ◽

2020 ◽

Vol 9 (1) ◽

pp. 107 ◽

Cited By ~ 2

Author(s):

Heoncheol Lee

Keyword(s):

Gaussian Mixture Models ◽

Search Space ◽

Gaussian Mixture ◽

Transformation Matrix ◽

Robot Systems ◽

Feature Based ◽

The Individual ◽

Merging Method ◽

Multi Robot ◽

Map Merging

Multi-robot systems require collective map information on surrounding environments to efficiently cooperate with one another on assigned tasks. This paper addresses the problem of grid map merging to obtain the collective map information in multi-robot systems with unknown initial poses. If inter-robot measurements are not available, the only way to merge the maps is to find and match the overlapping area between maps. This paper proposes a tomographic feature-based map merging method, which can be successfully conducted with relatively small overlapping areas. The first part of the proposed method is to estimate a map transformation matrix using the Radon transform which can extract tomographically salient features from individual grid maps. The second part is to determine the search space using Gaussian mixture models based on the estimated map transformation matrix. The final part is to optimize an objective function modeled from tomographic information within the determined search space. Evaluation results with various pairs of individual maps produced by simulations and experiments showed that the proposed method can merge the individual maps more accurately than other map merging methods.

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Mobility of Older Adults: Gait Quality Measures are associated with Life-Space Assessment Scores

The Journals of Gerontology Series A ◽

10.1093/gerona/glab151 ◽

2021 ◽

Author(s):

Anisha Suri ◽

Andrea L Rosso ◽

Jessie VanSwearingen ◽

Leslie M Coffman ◽

Mark S Redfern ◽

...

Keyword(s):

Older Adults ◽

Random Forest ◽

Gait Speed ◽

Real Life ◽

Quality Measures ◽

Gaussian Mixture ◽

Community Dwelling ◽

Life Space ◽

Time Frequency ◽

Gait Quality

Abstract Background The relation of gait quality to real-life mobility among older adults is poorly understood. This study examined the association between gait quality, consisting of step variability, smoothness, regularity, symmetry and gait speed with the Life-Space Assessment (LSA). Methods In community-dwelling older adults (N=232, age 77.5±6.6, 65% females), gait quality was derived from: a) an instrumented walkway: gait speed, variability and walk-ratio; and b) accelerometer: signal variability, smoothness, regularity, symmetry, and time-frequency spatiotemporal variables during 6-minute walk. In addition to collecting LSA scores, cognitive functioning, walking-confidence, and falls were recorded. Spearman correlations (speed as covariate) and Random Forest Regression were used to assess associations between gait quality and LSA, and Gaussian-mixture modeling (GMM) was used to cluster participants. Results Spearman correlations of ρp=0.11 (signal amplitude variability ML), ρp=0.15, ρp=-0.13 (symmetry AP-V, ML-AP), ρp=0.16 (power V) and ρ=0.26 (speed), all p<0.05 and marginally related, ρp=-0.12 (regularity V), ρp=0.11 (smoothness AP) and ρp=-0.11 (step-time variability), p<0.1 were obtained. The cross-validated Random Forest model indicated good fit LSA prediction error of 17.77; gait and cognition were greater contributors than age and gender. GMM indicated two clusters. Group-1(N=189) had better gait quality than Group-2(N=43): greater smoothness AP (2.94±0.75 vs 2.30±0.71); greater similarity AP-V (0.58±0.13 vs 0.40±0.19); lower regularity V (0.83±0.08 vs 0.87±0.10); greater power V (1.86±0.18 vs 0.97±1.84); greater speed (1.09±0.16 vs 1.00±0.16 m/s); lower step time CoV (3.70±1.09 vs 5.09±2.37) and better LSA (76±18 vs 67±18), padjusted<0.004. Conclusions Gait quality measures taken in the clinic are associated with real-life mobility in the community.

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