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

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

scholarly journals Real-Time Audio Retrieval Method and Automatic Commercial Detecting System

2006 ◽  
Vol 2 (3) ◽  
pp. 297-302 ◽  
Author(s):  
Guibin Zheng ◽  
Jiqing Han
Keyword(s):  
Real Time ◽  
Retrieval Method ◽  
Audio Retrieval

scholarly journals Robust audio retrieval method based on anti‐noise fingerprinting and segmental matching

2020 ◽  
Vol 56 (5) ◽  
pp. 245-247 ◽  
Author(s):  
Xueshuai Zhang ◽  
Ge Zhan ◽  
Wenchao Wang ◽  
Pengyuan Zhang ◽  
Yonghong Yan
Keyword(s):  
Retrieval Method ◽  
Audio Retrieval

scholarly journals Audio Fingerprint Extraction Based on Locally Linear Embedding for Audio Retrieval System

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1483
Author(s):  
Maoshen Jia ◽  
Tianhao Li ◽  
Jing Wang
Keyword(s):  
Locally Linear Embedding ◽  
Time Warping ◽  
Retrieval Method ◽  
Audio Fingerprinting ◽  
Audio Retrieval ◽  
Audio Data ◽  
Linear Embedding ◽  
Matching Strategy ◽  
Dynamic Time ◽  
Locally Linear

With the appearance of a large amount of audio data, people have a higher demand for audio retrieval, which can quickly and accurately find the required information. Audio fingerprint retrieval is a popular choice because of its excellent performance. However, there is a problem about the large amount of audio fingerprint data in the existing audio fingerprint retrieval method which takes up more storage space and affects the retrieval speed. Aiming at the problem, this paper presents a novel audio fingerprinting method based on locally linear embedding (LLE) that has smaller fingerprints and the retrieval is more efficient. The proposed audio fingerprint extraction divides the bands around each peak in the frequency domain into four groups of sub-regions and the energy of every sub-region is computed. Then the LLE is performed for each group, respectively, and the audio fingerprint is encoded by comparing adjacent energies. To solve the distortion of linear speed changes, a matching strategy based on dynamic time warping (DTW) is adopted in the retrieval part which can compare two audio segments with different lengths. To evaluate the retrieval performance of the proposed method, the experiments are carried out under different conditions of single and multiple groups’ dimensionality reduction. Both of them can achieve a high recall and precision rate and has a better retrieval efficiency with less data compared with some state-of-the-art methods.

scholarly journals A Two-Stage Audio Retrieval Method for Searching Unannotated Audio Clips

2008 ◽  
Author(s):  
Songhua Xu ◽  
Suchao Chen ◽  
Kevin Y. Yip ◽  
Francis C. M. Lau ◽  
Xueying Qin
Keyword(s):  
Two Stage ◽  
Retrieval Method ◽  
Audio Retrieval

A Novel Quick Audio Retrieval Method Based on Similarity

2009 ◽  
Vol 9 (1) ◽  
pp. 164-168
Author(s):  
Jinglong Wu ◽  
Guanghui Ren ◽  
Peng Li
Keyword(s):  
Retrieval Method ◽  
Audio Retrieval

Information Technology and an Audio Retrieval Method Based on a Novel Audience Rating System

2014 ◽  
Vol 886 ◽  
pp. 664-667
Author(s):  
Lan Tian ◽  
Qing Hua Song ◽  
Xiao Shan Lu
Keyword(s):  
Recognition Rate ◽  
Audio Signal ◽  
Rating System ◽  
Test Results ◽  
Simulation Test ◽  
Retrieval Method ◽  
Vector Correlation ◽  
Audio Features ◽  
Audio Retrieval ◽  
Audience Rating

A novel and universal audience rating system based on TV audio features is introduced. In this system, the audio signal is sampled from the audio outlet of TV set and high compressed into a specific spectrum features packages. The packaged audio features are high robustness for different types of television sets and sound volumes, and have no disturbance of surroundings. In the channel audio retrieval method, feature vector correlation analysis and pattern matching are adopted successively. The simulation test results show that the TV channel recognition rate is above 93%, and other sounds from non-TV channels can be detected reliably.

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