This study proposes an audio copy detection system that is robust to various attacks. These include the severe pitch shift and tempo change attacks which existing systems fail to detect. First, we propose a novel two dimensional representation for audio signals called the time-chroma image. This image is based on a modification of the concept of chroma in the music literature and is shown to achieve better performance in song identification. Then, we propose a novel fingerprinting algorithm that extracts local fingerprints from the time-chroma image. The proposed local fingerprinting algorithm is invariant to time frequency scale changes in audio signals. It also outperforms existing methods like SIFT by a great extent. Finally, we introduce a song identification algorithm that uses the proposed fingerprints. The resulting copy detection system is shown to significantly outperform existing methods. Besides being able to detect whether a song (or a part of it) has been copied, the proposed system can accurately estimate the amount of pitch shift and or tempo change that might have been applied to a song.
Probably the most well-known publicly available audio fingerprinting algorithm is Shazam @cite_11 . Shazam, is based on local audio fingerprints. With Shazam, people can find the song they are looking for, using their smart phones. Shazam uses the peaks (maxima) observed in the spectrogram of an audio signal as the local feature points of a song. Feature descriptors (fingerprints) are then generated from the attributes of pairs of these points. The frequency of every point in each pair as well as their time difference form a compact fingerprint for each pair. The extracted fingerprints are shown to be highly robust to audio compression, foreground noises, and other types of noise. However, they are not robust to tempo changes or pitch shifts.
@cite_11
122994913
We have developed and commercially deployed a flexible audio search engine. The algorithm is noise and distortion resistant, computationally efficient, and massively scalable, capable of quickly identifying a short segment of music captured through a cellphone microphone in the presence of foreground voices and other dominant noise, and through voice codec compression, out of a database of over a million tracks. The algorithm uses a combinatorially hashed time-frequency constellation analysis of the audio, yielding unusual properties such as transparency, in which multiple tracks mixed together may each be identified. Furthermore, for applications such as radio monitoring, search times on the order of a few milliseconds per query are attained, even on a massive music database.