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A number of abrupt change detection methods have been proposed in the past, among which are efficient model-based techniques such as the Generalized Likelihood Ratio (GLR) test. We consider the case where no accurate nor tractable model can be found, using a model-free approach, called Kernel change detection (KCD). KCD compares two sets of descriptors(More)
Dirichlet Process Mixtures (DPMs) are a popular class of statistical models to perform density estimation and clustering. However, when the data available have a distribution evolving over time, such models are inadequate. We introduce here a class of time-varying DPMs which ensures that at each time step the random distribution follows a DPM model. Our(More)
This paper presents a procedure aimed at recognizing environmental sounds for surveillance and security applications. We propose to apply One-Class Support Vector Machines (1-SVMs) together with a sophisticated dissimilarity measure as a discriminative framework in order to address audio classification, and hence, sound recognition. We illustrate the(More)
This paper deals with the computational analysis of musical audio from recorded audio waveforms. This general problem includes, as subtasks, music transcription, extraction of musical pitch, dynamics, timbre, instrument identity, and source separation. Analysis of real musical signals is a highly ill-posed task which is made complicated by the presence of(More)
In this paper we present an efficient particle filtering method to perform optimal estimation in Jump Markov (Nonlinear) Systems (JMS). Such processes consist of a mixture of heterogeneous models and possess a natural hierarchical structure. We take advantage of these specificities in order to develop a generic filtering methodology for these models. The(More)
Estimating the pitch of musical signals is complicated by the presence of partials in addition to the fundamental frequency. In this paper, we propose developments to an earlier Bayesian model which describes each component signal in terms of fundamental frequency, partials ('harmonics'), and amplitude. This basic model is modified for greater realism to(More)
This paper addresses the joint estimation and detection of time-varying 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(More)
In this paper, we introduce an hybrid time-frequency/support vector machine algorithm for the detection of abrupt spectral changes. A stationarity index is derived from support vector novelty detection theory by using sub-images extracted from the time-frequency plane as feature vectors. Simulations show the efficiency of this new algorithm for audio signal(More)
We propose a joint segmentation algorithm for piecewise constant AR processes recorded by several independent sensors. The algorithm is based on a hierarchical Bayesian model. Appropriate priors allow to introduce correlations between the change locations of the observed signals. Numerical problems inherent to Bayesian inference are solved by a Gibbs(More)