Abstract
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.
Highlights
With the development of internet technology, people are in an age of information explosion.The amount of audio information increased sharply in 30 years, which means people are exposed to various information every day [1]
With the development of audio compression and storage technology, quantities of digital audio information have appeared on the internet which puts forward a higher requirement for the efficiency of audio features and gives rise to audio fingerprinting [7]
As to ensure the efficiency of audio retrieval is not seriously affected by the feature lost in the dimensionality reduction process, the algorithm does not reduce the dimension of energy vectors in all groups
Summary
With the development of internet technology, people are in an age of information explosion.The amount of audio information increased sharply in 30 years, which means people are exposed to various information every day [1]. With the development of internet technology, people are in an age of information explosion. In the field of audio retrieval, high-dimensional audio features reduce the searching efficiency because of their redundancy of information and large storage. These features are computed in the time or frequency domain [4]. With the development of audio compression and storage technology, quantities of digital audio information have appeared on the internet which puts forward a higher requirement for the efficiency of audio features and gives rise to audio fingerprinting [7]
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