Name
Abstract | ||
|---|---|---|
It is challenging to retrieve audio clips from large audio datasets not only due to the high dimensionality of audio but also due to the large number of audios. Fingerprinting methods primarily focus on the use of semantic-level techniques to speed up retrieval and neglect low-level support. This paper shows that the performance of audio retrieval can be exploited by properly organizing and manipulating audio fingerprint data. The proposed sampling and counting method markedly improves the retrieval speed while maintaining a high recall rate and high precision for short audio clips. The proposed inverted index structure for fingerprints quickly shrinks the scope of the candidate set while requiring considerably less memory. The experiments show that the proposed method is faster and yields more consistent performance in terms of recall rate and precision than do the state-of-the-art methods. The proposed method is scalable for big audio data and is robust to background noise, resampling, MP3 conversion, white noise addition, bandpass filtering, chorus, echo, flanger, gsm compression, and tremolo. The average precision and recall rates are 99.995% and 99.78%, respectively, for dataset 1 and average 98.97% and 95.15%, respectively, for dataset 2. Specifically, the precision and recall rates for query audio clips of 3 s in length in the targeted dataset 1 are 99.94% and 99.64%, respectively. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TMM.2017.2723846 | IEEE Trans. Multimedia |
Keywords | Field | DocType |
Fingerprint recognition,Filtering,Indexes,Acoustic distortion,Robustness | Background noise,Fingerprint recognition,Computer science,White noise,Artificial intelligence,Inverted index,Computer vision,Pattern recognition,Precision and recall,Filter (signal processing),Speech recognition,Fingerprint,Audio normalization | Journal |
Volume | Issue | ISSN |
19 | 9 | 1520-9210 |
Citations | PageRank | References |
2 | 0.43 | 13 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shanshan Yao | 1 | 21 | 3.71 |
| Baoning Niu | 2 | 53 | 7.37 |
| Jianquan Liu | 3 | 89 | 14.92 |