scholarly journals An Audio Retrieval Algorithm Based on Audio Shot and Inverted Index

Author(s):  
Xueyuan Zhang ◽  
Qianhua He
2013 ◽  
Vol 34 (11) ◽  
pp. 2561-2567 ◽  
Author(s):  
Xue-yuan Zhang ◽  
Qian-hua He ◽  
Yan-xiong Li ◽  
Wan-ling Ye

2014 ◽  
Vol 608-609 ◽  
pp. 304-308
Author(s):  
Qiang Chen

This paper introduces the basic structure of audio retrieval system based on content, and in the related literature at home and abroad, analyzes the main features of audio retrieval algorithm that divided into the following several types: minimum distance method, neural network, Support Vector Machine, decision tree search algorithm and other audio retrieval algorithm. At the same time, this paper discusses some key techniques of audio retrieval.


Author(s):  
Yue Song ◽  
Sha Tao ◽  
Yanzhao Ren ◽  
Xinliang Liu ◽  
Wanlin Gao

2010 ◽  
Vol 30 (1) ◽  
pp. 230-232
Author(s):  
Ji-chen YANG ◽  
Wei-ning WANG
Keyword(s):  

Author(s):  
Narina Thakur ◽  
Deepti Mehrotra ◽  
Abhay Bansal ◽  
Manju Bala

Objective: Since the adequacy of Learning Objects (LO) is a dynamic concept and changes in its use, needs and evolution, it is important to consider the importance of LO in terms of time to assess its relevance as the main objective of the proposed research. Another goal is to increase the classification accuracy and precision. Methods: With existing IR and ranking algorithms, MAP optimization either does not lead to a comprehensively optimal solution or is expensive and time - consuming. Nevertheless, Support Vector Machine learning competently leads to a globally optimal solution. SVM is a powerful classifier method with its high classification accuracy and the Tilted time window based model is computationally efficient. Results: This paper proposes and implements the LO ranking and retrieval algorithm based on the Tilted Time window and the Support Vector Machine, which uses the merit of both methods. The proposed model is implemented for the NCBI dataset and MAT Lab. Conclusion: The experiments have been carried out on the NCBI dataset, and LO weights are assigned to be relevant and non - relevant for a given user query according to the Tilted Time series and the Cosine similarity score. Results showed that the model proposed has much better accuracy.


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