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

2013 ◽  
pp. 164-167 ◽  
Author(s):  
Xueyuan Zhang ◽  
Qianhua He
Keyword(s):  
Inverted Index ◽  
Retrieval Algorithm ◽  
Audio Retrieval

An Inverted Index Based Audio Retrieval Method

2013 ◽  
Vol 34 (11) ◽  
pp. 2561-2567 ◽  
Author(s):  
Xue-yuan Zhang ◽  
Qian-hua He ◽  
Yan-xiong Li ◽  
Wan-ling Ye
Keyword(s):  
Inverted Index ◽  
Retrieval Method ◽  
Audio Retrieval

Research on Audio Retrieval Based on Content

2014 ◽  
Vol 608-609 ◽  
pp. 304-308
Author(s):  
Qiang Chen
Keyword(s):  
Minimum Distance ◽  
Search Algorithm ◽  
Basic Structure ◽  
Retrieval Algorithm ◽  
Support Vector ◽  
Distance Method ◽  
Network Support ◽  
Audio Retrieval ◽  
Key Techniques ◽  
Tree Search Algorithm

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.

scholarly journals An agricultural audio retrieval method based on inverted index of silence word

2020 ◽  
Vol 768 ◽  
pp. 052034
Author(s):  
Yue Song ◽  
Sha Tao ◽  
Yanzhao Ren ◽  
Xinliang Liu ◽  
Wanlin Gao
Keyword(s):  
Inverted Index ◽  
Retrieval Method ◽  
Audio Retrieval

Method of specific audio retrieval based on randomly segment

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

Simulation and Performance Analysis of Tilted Time Window and Support Vector Machine Based Learning Object Ranking Method

2020 ◽  
Vol 13 (2) ◽  
pp. 153-164
Author(s):  
Narina Thakur ◽  
Deepti Mehrotra ◽  
Abhay Bansal ◽  
Manju Bala
Keyword(s):  
Support Vector Machine ◽  
Classification Accuracy ◽  
Time Window ◽  
Optimal Solution ◽  
Similarity Score ◽  
Learning Objects ◽  
Retrieval Algorithm ◽  
Support Vector ◽  
Computationally Efficient ◽  
User Query

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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