The growing consumer base and expanding market for various music styles highlight the necessity of classifying music genres to cater to people's preferences. Manual music ranking is a labor-intensive process for l...
The growing consumer base and expanding market for various music styles highlight the necessity of classifying music genres to cater to people's preferences. Manual music ranking is a labor-intensive process for listeners, prompting the need for a more efficient approach. This involves the extraction of Mel-frequency cepstral coefficient feature maps from the log Mel-spectrograms of audio clips. The extracted feature maps are supplied to a multiclass semi-supervised deep convolutional generative adversarial network where the discriminator behaves as a classifier. The training of models involves utilizing the GTZAN standardized dataset, a publicly accessible collection of thousands of audio files spanning ten different genres, from which 80% and 20% of the data are used for training and testing, respectively. Finally, the paper discusses the performance of the semi-supervised deep convolutional generative adversarial network through the RMSprop and Adam optimizers on the original and augmented labeled and unlabeled MFCC feature maps. Without any data augmentation, the discriminator achieves a training accuracy of 97.9% and a test accuracy of about 45.67%. In contrast, the discriminator's training accuracy is about 98.3%, and the test accuracy is 84.75% with data augmentation.
Functional and mathematical models for the distribution of academic workload at the stage of preparing the educational process at a university are considered, which make it possible to largely determine the uniformity...
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ISBN:
(数字)9798350364378
ISBN:
(纸本)9798350364385
Functional and mathematical models for the distribution of academic workload at the stage of preparing the educational process at a university are considered, which make it possible to largely determine the uniformity of teachers' workload with various types of academic work when fulfilling the criteria and restrictions adopted for the distribution.
In this paper, we propose a novel player behavior model called the action priority model (APM) for representing player action behaviors. A play log is stored based on the game grammar under analysis, and heuristic fil...
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Matrix completion is one of the crucial tools in modern datascience research. Recently, a novel sampling model for matrix completion coined cross-concentrated sampling (CCS) has caught much attention. However, the ro...
Matrix completion is one of the crucial tools in modern datascience research. Recently, a novel sampling model for matrix completion coined cross-concentrated sampling (CCS) has caught much attention. However, the robustness of the CCS model against sparse outliers remains unclear in the existing studies. In this paper, we aim to answer this question by exploring a novel Robust CCS Completion problem. A highly efficient non-convex iterative algorithm, dubbed Robust CUR Completion (RCURC), is proposed. The empirical performance of the proposed algorithm, in terms of both efficiency and robustness, is verified in synthetic and real datasets.
In a recent breakthrough, Kelley and Meka (FOCS 2023) obtained a strong upper bound on the density of sets of integers without non-trivial three-term arithmetic progressions. In this work, we extend their result, esta...
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The project “AI-Based Aircraft Recognition System” aims to develop an advanced system for automatically recognizing and identifying aircraft using AI techniques. The increasing role of artificial intelligence (AI) i...
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ISBN:
(数字)9798350391244
ISBN:
(纸本)9798350391251
The project “AI-Based Aircraft Recognition System” aims to develop an advanced system for automatically recognizing and identifying aircraft using AI techniques. The increasing role of artificial intelligence (AI) in various sectors, including aviation, has driven the need for more efficient and accurate recognition systems. Aircraft recognition has attracted attention due to its potential uses in areas like military operations, air traffic control, and aviation surveillance. This project proposes the use of advanced AI algorithms to address gaps in current aircraft recognition systems by enhancing accuracy, performance, and real-time application.
This paper proposes a computer vision-based workflow that analyses Google 360-degree street views to understand the quality of urban spaces regarding vegetation coverage and accessibility of urban amenities such as be...
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Diffusion models have revolutionized various application domains, including computer vision and audio generation. Despite the state-of-the-art performance, diffusion models are known for their slow sample generation d...
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Adaptive learning in multiagent systems has emerged as a promising approach to enhance agents' capabilities to adapt to dynamic environments and optimize their performance. In this research paper, we investigate t...
Adaptive learning in multiagent systems has emerged as a promising approach to enhance agents' capabilities to adapt to dynamic environments and optimize their performance. In this research paper, we investigate the integration of brain-computer interfaces (BCIs) as a novel means to facilitate adaptive learning in multiagent systems. BCIs establish direct communication channels between the human brain and external devices, enabling real-time monitoring and interpretation of neural activity. By leveraging BCIs, agents can capture valuable cognitive signals from human operators and use this information to adapt their decision-making processes and behaviors. We present a theoretical framework that outlines the incorporation of BCIs into multiagent systems and the mechanisms for adaptive learning. Additionally, we propose a comprehensive methodology for evaluating the effectiveness of this approach. The results of our experiments demonstrate the potential of adaptive learningin multiagent systems using BCIs, paving the way for new applications in various domains, including human-machine col-laboration, assistive technologies, and interactive gaming systems.
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