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.
Serra @cite_16 have proposed a global audio feature extraction algorithm based on a two dimensional representation of the audio signal which we call the time-chroma image. The time-chroma represents the chroma content @cite_4 of an audio signal over time. Through extensive experiments Serra have shown that the time-chroma representation of an audio signal is a promising platform for designing audio detection algorithms. However, the use of global features does not extend this method to mash-up attacks. We will discuss the time-chroma representation later in this paper.
@cite_16 @cite_4
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We address the problem of blind audio source separation in the under-determined and convolutive case. The contribution of each source to the mixture channels in the time-frequency domain is modeled by a zero-mean Gaussian random vector with a full rank covariance matrix composed of two terms: a variance which represents the spectral properties of the source and which is modeled by a nonnegative matrix factorization (NMF) model and another full rank covariance matrix which encodes the spatial properties of the source contribution in the mixture. We address the estimation of these parameters by maximizing the likelihood of the mixture using an expectation-maximization (EM) algorithm. Theoretical propositions are corroborated by experimental studies on stereo reverberant music mixtures. Abstract In this paper, we describe the concept of music scene description and address the problem of detecting melody and bass lines in real-world audio signals containing the sounds of various instruments. Most previous pitch-estimation methods have had difficulty dealing with such complex music signals because these methods were designed to deal with mixtures of only a few sounds. To enable estimation of the fundamental frequency (F0) of the melody and bass lines, we propose a predominant-F0 estimation method called PreFEst that does not rely on the unreliable fundamental component and obtains the most predominant F0 supported by harmonics within an intentionally limited frequency range. This method estimates the relative dominance of every possible F0 (represented as a probability density function of the F0) by using MAP (maximum a posteriori probability) estimation and considers the F0’s temporal continuity by using a multiple-agent architecture. Experimental results with a set of ten music excerpts from compact-disc recordings showed that a real-time system implementing this method was able to detect melody and bass lines about 80 of the time these existed. 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. In this paper, we investigate the speech feature extraction problem in the noisy environment. A novel approach based on Gabor filtering and tensor factorization is proposed. From recent physiological and psychoacoustic experimental results, localized spectro-temporal features are essential for auditory perception. We employ 2D-Gabor functions with different scales and directions to analyze the localized patches of power spectrogram, by which speech signal can be encoded as a general higher order tensor. Then Nonnegative Tensor PCA with sparse constraint is used to learn the projection matrices from multiple interrelated feature subspaces and extract the robust features. Experimental results confirm that our proposed method can improve the speech recognition performance, especially in noisy environment, compared with traditional speech feature extraction methods.