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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...
This paper proposes a sampling and counting method, which remarkably improves retrieval speed yet maintaining high recall rate and precision for short audio clips retrieval. A new inverted index structure is proposed and it can quickly shrink the scope of the candidate set and save 59% memory compared with the related ones. The experiments conducted on 500K audios show that the method we proposed...
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