Low-order auditory Zernike moment: a novel approach for robust music identification in the compressed domain

Abstract

Audio identification via fingerprint has been an active research field for years. However, most previously reported methods work on the raw audio format in spite of the fact that nowadays compressed format audio, especially MP3 music, has grown into the dominant way to store music on personal computers and/or transmit it over the Internet. It will be interesting if a compressed unknown audio fragment could be directly recognized from the database without decompressing it into the wave format at first. So far, very few algorithms run directly on the compressed domain for music information retrieval, and most of them take advantage of the modified discrete cosine transform coefficients or derived cepstrum and energy type of features. As a first attempt, we propose in this paper utilizing compressed domain auditory Zernike moment adapted from image processing techniques as the key feature to devise a novel robust audio identification algorithm. Such fingerprint exhibits strong robustness, due to its statistically stable nature, against various audio signal distortions such as recompression, noise contamination, echo adding, equalization, band-pass filtering, pitch shifting, and slight time scale modification. Experimental results show that in a music database which is composed of 21,185 MP3 songs, a 10-s long music segment is able to identify its original near-duplicate recording, with average top-5 hit rate up to 90% or above even under severe audio signal distortions.

Audio Music Monitoring: Analyzing Current Techniques for Song Recognition and Identification

“…they are working on raw (uncompressed) audio formats like wav. But we all know that, nowadays compressed audio format like MP3 music, has grown into the dominant way to store music on personal computers and/or transmit it over the Internet [17]. Therefore it will be very nice if we can directly recognize compressed audio without decompressing it, and definitely, it will be more efficient and more accurate.…”

“…We are not going to discuss about this in very detail. You can find more information in [17]. For completeness we will show how to grab Zernike moment in image.…”

“…For completeness we will show how to grab Zernike moment in image. Following are extracted from [17].…”

“…We can see that there are several ways to do this according to the past researches. For example some we can construct a series of consecutive granule-MDCT 2D images [17].…”

Robust Audio Fingerprinting Method Using Prominent Peak Pair Based on Modulated Complex Lapped Transform

“…Common uses include query-by-example music or advertisement identification [4]- [5], broadcast monitoring [6], copyright detection, filtering for file sharing, and automatic audio-based content library organization [7].…”