Multimodal Information Fusion of Audio Emotion Recognition Based on Kernel Entropy Component Analysis

Xie, Zhibing; Guan, Ling

doi:10.1142/s1793351x13400023

Cited by 25 publications

(10 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although the initial motivation of the developed method was to improve the efficiency of nonparametric foreground segmentation strategies, it must be noted that it can be also used in many other scientific fields such as computer vision [24], [25] or audio signal processing [26], [27], where the evaluation of continuous mathematical functions constitutes a significant computational burden.…”

Section: A Contributionmentioning

confidence: 99%

Optimal Piecewise Linear Function Approximation for GPU-Based Applications

Berjón

Gallego

Cuevas

et al. 2016

IEEE Trans. Cybern.

View full text Add to dashboard Cite

Many computer vision and human-computer interaction applications developed in recent years need evaluating complex and continuous mathematical functions as an essential step toward proper operation. However, rigorous evaluation of these kind of functions often implies a very high computational cost, unacceptable in real-time applications. To alleviate this problem, functions are commonly approximated by simpler piecewise-polynomial representations. Following this idea, we propose a novel, efficient, and practical technique to evaluate complex and continuous functions using a nearly optimal design of two types of piecewise linear approximations in the case of a large budget of evaluation subintervals. To this end, we develop a thorough error analysis that yields asymptotically tight bounds to accurately quantify the approximation performance of both representations. It provides an improvement upon previous error estimates and allows the user to control the tradeoff between the approximation error and the number of evaluation subintervals. To guarantee real-time operation, the method is suitable for, but not limited to, an efficient implementation in modern graphics processing units, where it outperforms previous alternative approaches by exploiting the fixed-function interpolation routines present in their texture units. The proposed technique is a perfect match for any application requiring the evaluation of continuous functions; we have measured in detail its quality and efficiency on several functions, and, in particular, the Gaussian function because it is extensively used in many areas of computer vision and cybernetics, and it is expensive to evaluate.

show abstract

Section: A Contributionmentioning

confidence: 99%

Optimal Piecewise Linear Function Approximation for GPU-Based Applications

Berjón

Gallego

Cuevas

et al. 2016

IEEE Trans. Cybern.

View full text Add to dashboard Cite

show abstract

“…It integrates the information in multiple modalities and therefore is expected to perform better prediction than the case using any unimodal information [1]. Nowadays it has been applied in a broad range of applications, such as multimedia event detection [2,3], sentiment analysis [1,4], cross-modal translation [5][6][7], Visual Question Answering (VQA) [8,9], etc.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Modal Fusion Method Based on Higher-Order Orthogonal Iteration Decomposition

Fen

Chen

Tan

et al. 2021

Entropy

View full text Add to dashboard Cite

Multi-modal fusion can achieve better predictions through the amalgamation of information from different modalities. To improve the performance of accuracy, a method based on Higher-order Orthogonal Iteration Decomposition and Projection (HOIDP) is proposed, in the fusion process, higher-order orthogonal iteration decomposition algorithm and factor matrix projection are used to remove redundant information duplicated inter-modal and produce fewer parameters with minimal information loss. The performance of the proposed method is verified by three different multi-modal datasets. The numerical results validate the accuracy of the performance of the proposed method having 0.4% to 4% improvement in sentiment analysis, 0.3% to 8% improvement in personality trait recognition, and 0.2% to 25% improvement in emotion recognition at three different multi-modal datasets compared with other 5 methods.

show abstract

“…A similar technique by Zhang et al (2016) also used global covariance PCA for feature fusion in the i-vector system. Other examples can be found in the literature on the use of PCA for speech feature fusion such as (Chibelushi et al, 1997), (Lee and Narayanan, 2005) and (Xie and Guan, 2013).…”

Section: Introductionmentioning

confidence: 99%