Automated Voice Recognition of Emotions Through the Use of Neural Networks

Kurbesov, A. V.; Ryabkin, D. V.; Miroshnichenko, I. I.; Aruchidi, N. A.; Kalugyan, Karine Kh.

doi:10.1007/978-3-030-35249-3_87

Cited by 2 publications

(1 citation statement)

References 4 publications

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“…Industry strongly benefits from that by applying more and more advanced AI solutions in their products. The biggest industrial beneficiaries of the neural net-works are medicine and health care [10,11,12,13,14,15], banking and finances [16,17,18], but also safety [19,20,21,22,23] and entertainment [24,25,26].…”

Section: Introductionmentioning

confidence: 99%

Local Levenberg-Marquardt Algorithm for Learning Feedforwad Neural Networks

Bilski

Kowalczyk

Marchlewska

et al. 2020

Journal of Artificial Intelligence and Soft Computing Research

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This paper presents a local modification of the Levenberg-Marquardt algorithm (LM). First, the mathematical basics of the classic LM method are shown. The classic LM algorithm is very efficient for learning small neural networks. For bigger neural networks, whose computational complexity grows significantly, it makes this method practically inefficient. In order to overcome this limitation, local modification of the LM is introduced in this paper. The main goal of this paper is to develop a more complexity efficient modification of the LM method by using a local computation. The introduced modification has been tested on the following benchmarks: the function approximation and classification problems. The obtained results have been compared to the classic LM method performance. The paper shows that the local modification of the LM method significantly improves the algorithm’s performance for bigger networks. Several possible proposals for future works are suggested.

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Section: Introductionmentioning

confidence: 99%

Local Levenberg-Marquardt Algorithm for Learning Feedforwad Neural Networks

Bilski

Kowalczyk

Marchlewska

et al. 2020

Journal of Artificial Intelligence and Soft Computing Research

View full text Add to dashboard Cite

show abstract

Fast Computational Approach to the Levenberg-Marquardt Algorithm for Training Feedforward Neural Networks

Bilski

Smoląg

Kowalczyk

et al. 2023

Journal of Artificial Intelligence and Soft Computing Research

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This paper presents a parallel approach to the Levenberg-Marquardt algorithm (LM). The use of the Levenberg-Marquardt algorithm to train neural networks is associated with significant computational complexity, and thus computation time. As a result, when the neural network has a big number of weights, the algorithm becomes practically ineffective. This article presents a new parallel approach to the computations in Levenberg-Marquardt neural network learning algorithm. The proposed solution is based on vector instructions to effectively reduce the high computational time of this algorithm. The new approach was tested on several examples involving the problems of classification and function approximation, and next it was compared with a classical computational method. The article presents in detail the idea of parallel neural network computations and shows the obtained acceleration for different problems.

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Automated Voice Recognition of Emotions Through the Use of Neural Networks

Cited by 2 publications

References 4 publications

Local Levenberg-Marquardt Algorithm for Learning Feedforwad Neural Networks

Local Levenberg-Marquardt Algorithm for Learning Feedforwad Neural Networks

Fast Computational Approach to the Levenberg-Marquardt Algorithm for Training Feedforward Neural Networks

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