Results 1  10
of
17
Semisupervised linear spectral unmixing using a hierarchical Bayesian model for hyperspectral imagery,” IRIT/ENSEEIHT/TeSA
, 2007
"... Abstract—This paper proposes a hierarchical Bayesian model that can be used for semisupervised hyperspectral image unmixing. The model assumes that the pixel reflectances result from linear combinations of pure component spectra contaminated by an additive Gaussian noise. The abundance parameters a ..."
Abstract

Cited by 31 (21 self)
 Add to MetaCart
Abstract—This paper proposes a hierarchical Bayesian model that can be used for semisupervised hyperspectral image unmixing. The model assumes that the pixel reflectances result from linear combinations of pure component spectra contaminated by an additive Gaussian noise. The abundance parameters appearing in this model satisfy positivity and additivity constraints. These constraints are naturally expressed in a Bayesian context by using appropriate abundance prior distributions. The posterior distributions of the unknown model parameters are then derived. A Gibbs sampler allows one to draw samples distributed according to the posteriors of interest and to estimate the unknown abundances. An extension of the algorithm is finally studied for mixtures with unknown numbers of spectral components belonging to a know library. The performance of the different unmixing strategies is evaluated via simulations conducted on synthetic and real data. Index Terms—Gibbs sampler, hierarchical Bayesian analysis, hyperspectral images, linear spectral unmixing, Markov chain Monte Carlo (MCMC) methods, reversible jumps. I.
Analysis of polyphonic audio using sourcefilter model and
"... nonnegative matrix factorization ..."
Joint Detection and Tracking of TimeVarying Harmonic Components: a Flexible Bayesian Approach
 in "IEEE transactions on Speech, Audio and Language Processing
, 2006
"... This paper addresses the joint estimation and detection of timevarying harmonic components in audio signals. We follow a flexible viewpoint, where several frequency/amplitude trajectories are tracked in spectrogram using particle filtering. The core idea is that each harmonic component (composed of ..."
Abstract

Cited by 12 (0 self)
 Add to MetaCart
This paper addresses the joint estimation and detection of timevarying harmonic components in audio signals. We follow a flexible viewpoint, where several frequency/amplitude trajectories are tracked in spectrogram using particle filtering. The core idea is that each harmonic component (composed of a fundamental partial together with several overtone partials) is considered a target. Tracking requires to define a statespace model with state transition and measurement equations. Particle filtering algorithms rely on a socalled sequential importance distribution, and we show that it can be built on previous multipitch estimation algorithms, so as to yield an even more efficient estimation procedure with established convergence properties. Moreover, as our model captures all the harmonic model information, it actually separates the harmonic sources. Simulations on synthetic and real music data show the interest of our approach.
Automatic transcription of piano music based on HMM tracking of jointlyestimated pitches
 in Proc of the 4th Music Information Retrieval Evaluation eXchange (MIREX
, 2008
"... This work deals with the automatic transcription of piano recordings into a MIDI symbolic file. The system consists of subsequent stages of onset detection and multipitch estimation and tracking. The latter is based on a Hidden Markov Model framework, embedding a spectral maximum likelihood method f ..."
Abstract

Cited by 8 (3 self)
 Add to MetaCart
This work deals with the automatic transcription of piano recordings into a MIDI symbolic file. The system consists of subsequent stages of onset detection and multipitch estimation and tracking. The latter is based on a Hidden Markov Model framework, embedding a spectral maximum likelihood method for joint pitch estimation. The complexity issue of joint estimation techniques is solved by selecting subsets of simultaneously played notes within a preestimated set of candidates. Tests on a large database and comparisons to stateoftheart methods show promising results. 1.
Estimating the number of endmembers in hyperspectral images using the normal compositional model and a hierarchical Bayesian algorithm
 Department of Electrical Engineering and Computer Science, University of Michigan, Ann
, 1984
"... Abstract—This paper studies a semisupervised Bayesian unmixing algorithm for hyperspectral images. This algorithm is based on the normal compositional model recently introduced by Eismann and Stein. The normal compositional model assumes that each pixel of the image is modeled as a linear combinati ..."
Abstract

Cited by 5 (5 self)
 Add to MetaCart
Abstract—This paper studies a semisupervised Bayesian unmixing algorithm for hyperspectral images. This algorithm is based on the normal compositional model recently introduced by Eismann and Stein. The normal compositional model assumes that each pixel of the image is modeled as a linear combination of an unknown number of pure materials, called endmembers. However, contrary to the classical linear mixing model, these endmembers are supposed to be random in order to model uncertainties regarding their knowledge. This paper proposes to estimate the mixture coefficients of the Normal Compositional Model (referred to as abundances) as well as their number using a reversible jump Bayesian algorithm. The performance of the proposed methodology is evaluated thanks to simulations conducted on synthetic and real AVIRIS images. Index Terms—Bayesian inference, hyperspectral images, Monte Carlo methods, normal compositional model, reversible jump,
Unsupervised singlechannel music source separation by average harmonic structure modeling
 IEEE Trans. Audio Speech Language Process., submitted
"... Source separation of musical signals is an appealing but difficult problem, especially in the singlechannel case. In this paper, an unsupervised singlechannel music source separation algorithm based on average harmonic structure modeling is proposed. Under the assumption of playing in narrow pitch ..."
Abstract

Cited by 5 (2 self)
 Add to MetaCart
Source separation of musical signals is an appealing but difficult problem, especially in the singlechannel case. In this paper, an unsupervised singlechannel music source separation algorithm based on average harmonic structure modeling is proposed. Under the assumption of playing in narrow pitch ranges, different harmonic instrumental sources in a piece of music often have different but stable harmonic structures, thus sources can be characterized uniquely by harmonic structure models. Given the number of instrumental sources, the proposed algorithm learns these models directly from the mixed signal by clustering the harmonic structures extracted from different frames. The corresponding sources are then extracted from the mixed signal using the models. Experiments on several mixed signals, including synthesized instrumental sources, real instrumental sources and singing voices, show that this algorithm outperforms the general Nonnegative Matrix Factorization (NMF)based source separation algorithm, and yields good subjective listening quality. As a sideeffect, this algorithm estimates the pitches of the harmonic instrumental sources. The number of concurrent sounds in each frame is also computed, which
Bayesian audio source separation
"... In this chapter we describe a Bayesian approach to audio source separation. The approach relies on probabilistic modeling of sound sources as (sparse) linear combinations of atoms from a dictionary and Markov chain Monte Carlo (MCMC) inference. Several prior distributions are considered for the sour ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
In this chapter we describe a Bayesian approach to audio source separation. The approach relies on probabilistic modeling of sound sources as (sparse) linear combinations of atoms from a dictionary and Markov chain Monte Carlo (MCMC) inference. Several prior distributions are considered for the source expansion coefficients. We first consider independent and identically distributed (iid) general priors with two choices of distributions. The first one is the Student t, which is a good model for sparsity when the shape parameter has a low value. The second one is a hierarchical mixture distribution; conditionally upon an indicator variable, one coefficient is either set to zero or given a normal distribution, whose variance is in turn given an invertedGamma distribution. Then, we consider more audiospecific models where both the identically distributed and independently distributed assumptions are lifted. Using a Modified Discrete Cosine Transform (MDCT) dictionary, a timefrequency orthonormal basis, we describe frequencydependent structured priors which explicitly model the harmonic structure of sound, using a Markov hierarchical modeling of the expansion coefficients. Separation results are given for a stereophonic recording of 3 sources. 1
Bayesian Interpolation and Parameter Estimation in a Dynamic Sinusoidal Model
"... Abstract—In this paper, we propose a method for restoring the missing or corrupted observations of nonstationary sinusoidal signals which are often encountered in music and speech applications. To model nonstationary signals, we use a timevarying sinusoidal model which is obtained by extending the ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
Abstract—In this paper, we propose a method for restoring the missing or corrupted observations of nonstationary sinusoidal signals which are often encountered in music and speech applications. To model nonstationary signals, we use a timevarying sinusoidal model which is obtained by extending the static sinusoidal model into a dynamic sinusoidal model. In this model, the inphase and quadrature components of the sinusoids are modeled as firstorder Gauss–Markov processes. The inference scheme for the model parameters and missing observations is formulated in a Bayesian framework and is based on a Markov chain Monte Carlo method known as Gibbs sampler. We focus on the parameter estimation in the dynamic sinusoidal model since this constitutes the core of modelbased interpolation. In the simulations, we first investigate the applicability of the model and then demonstrate the inference scheme by applying it to the restoration of lost audio packets on a packetbased network. The results show that the proposed method is a reasonable inference scheme for estimating unknown signal parameters and interpolating gaps consisting of missing/corrupted signal segments. Index Terms—Bayesian signal processing, sinusoidal signal model, state space modeling. I.
Automatic Transcription of Pitch Content in Music and Selected Applications
"... Transcription of music refers to the analysis of a music signal in order to produce a parametric representation of the sounding notes in the signal. This is conventionally carried out by listening to a piece of music and writing down the symbols of common musical notation to represent the occurring ..."
Abstract
 Add to MetaCart
Transcription of music refers to the analysis of a music signal in order to produce a parametric representation of the sounding notes in the signal. This is conventionally carried out by listening to a piece of music and writing down the symbols of common musical notation to represent the occurring notes in the piece. Automatic transcription of music refers to the extraction of such representations using signalprocessing methods. This thesis concerns the automatic transcription of pitched notes in musical audio and its applications. Emphasis is laid on the transcription of realistic polyphonic music, where multiple pitched and percussive instruments are sounding simultaneously. The methods included in this thesis are based on a framework which combines both lowlevel acoustic modeling and highlevel musicological modeling. The emphasis in the acoustic modeling has been set to note events so that the methods produce discretepitch notes with onset times and durations
Model Considerations for Memorybased Automatic Music Transcription
"... Abstract. The problem of automatic music description is considered. The recorded music is modeled as a superposition of known sounds from a library weighted by unknown weights. Similar observation models are commonly used in statistics and machine learning. Many methods for estimation of the weights ..."
Abstract
 Add to MetaCart
Abstract. The problem of automatic music description is considered. The recorded music is modeled as a superposition of known sounds from a library weighted by unknown weights. Similar observation models are commonly used in statistics and machine learning. Many methods for estimation of the weights are available. These methods differ in the assumptions imposed on the weights. In Bayesian paradigm, these assumptions are typically expressed in the form of prior probability density function (pdf) on the weights. In this paper, commonly used assumptions about music signal are summarized and complemented by a new assumption. These assumptions are translated into pdfs and combined into a single prior density using combination of pdfs. Validity of the model is tested in simulation using synthetic data.