Learn More
Multiple pitch estimation consists of estimating the fundamental frequencies and saliences of pitched sounds over short time frames of an audio signal. This task forms the basis of several applications in the particular context of musical audio. One approach is to decompose the short-term magnitude spectrum of the signal into a sum of basis spectra(More)
A new method for the estimation of multiple concurrent pitches in piano recordings is presented. It addresses the issue of overlapping overtones by modeling the spectral envelope of the overtones of each note with a smooth autoregressive model. For the background noise, a moving-average model is used and the combination of both tends to eliminate harmonic(More)
This paper introduces a fast implementation of the power iteration method for subspace tracking, based on an approximation that is less restrictive than the well-known projection approximation. This algorithm, referred to as the fast approximated power iteration (API) method, guarantees the orthonormality of the subspace weighting matrix at each iteration.(More)
This paper presents theoretical and experimental results about constrained non-negative matrix factorization (NMF) in a Bayesian framework. A model of superimposed Gaussian components including harmonicity is proposed, while temporal continuity is enforced through an inverse-Gamma Markov chain prior. We then exhibit a space-alternating generalized(More)
We address the issue of underdetermined source separation in a particular informed configuration where both the sources and the mixtures are known during a so-called encoding stage. This knowledge enables the computation of a side-information which is small enough to be inaudibly embedded into the mixtures. At the decoding stage, the sources are no longer(More)
In this paper we present a new technique for monaural source separation in musical mixtures, which uses the knowledge of the musical score. This information is used to initialize an algorithm which computes a parametric decomposition of the spectrogram based on non-negative matrix factorization (NMF). This algorithm provides time-frequency masks which are(More)
High-resolution methods such as the ESPRIT algorithm are of major interest for estimating discrete spectra, since they overcome the resolution limit of the Fourier transform and provide very accurate estimates of the signal parameters. In signal processing literature, most contributions focus on the estimation of exponentially modulated sinusoids in a noisy(More)
High resolution methods, such as the ESPRIT (estimation of signal parameters by rotational invariance techniques) algorithm, perform an accurate representation of a harmonic signal as a sum of exponentially damped sinusoids. However, in coding applications, the signal must be represented with a minimum number of parameters. Unfortunately, it is well known(More)
Real world sounds often exhibit non-stationary spectral characteristics such as those produced by a harpsichord or a guitar. The classical Non-negative Matrix Factorization (NMF) needs a number of atoms to accurately decompose the spectrogram of such sounds. An extension of NMF is proposed hereafter which includes time-frequency activations based on ARMA(More)
This paper introduces a new algorithm for tracking the major subspace of the correlation matrix associated with time series. This algorithm greatly outperforms many well-known subspace track-ers in terms of subspace estimation. Moreover, it guarantees the orthonormality of the subspace weighting matrix at each iteration, and reaches the lowest complexity(More)