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In recent years, Compressive Sampling (CS), a new research topic in signal processing, has piqued interest of a wide range of researchers in different fields. In this paper, we present a sub-Nyquist Audio Fingerprinting (AF) system for music recognition, which utilizes CS theory to generate a compact audio fingerpint, and to achieve significant reduction of the dimensionality of the input signal. The presented experimental results demonstrate that by using the CS-based sub-Nyquist AF system, when downsampling to 30%, the average accuracy is 93.43% under various distorted environments, compared to Nyquist sampling methods. The advantages of the proposed process lie in the comparable performance under the sub-Nyquist sampling rate, and more compact audio fingerprint.
Note: This article was mistakenly omitted from the original IEEE Xplore conference submission.