A new framework for representing quasi-harmonic signals, and its application to score-informed single channel musical instruments separation, is introduced in this paper. In the proposed approach, the signal's pitch and spectral envelope are modeled separately. The model combines parametric filters enforcing an harmonic structure in the representation, with Gaussian modeling for representing the spectral envelope. The estimation of the signal's model is cast as an inverse problem efficiently solved via a maximum a posteriori expectation-maximization algorithm. The relation of the proposed framework with common non-negative factorization methods is also discussed. The algorithm is evaluated with both real and synthetic instruments mixtures, and comparisons with recently proposed techniques are presented.

A collaborative framework for detecting the different sources in mixed signals is presented in this paper. The approach is based on C-HiLasso, a convex collaborative hierarchical sparse model, and proceeds as follows. First, we build a structured dictionary for mixed signals by concatenating a set of sub-dictionaries, each one of them learned to sparsely model one of a set of possible classes. Then, the coding of the mixed signal is performed by efficiently solving a convex optimization problem that combines standard sparsity with group and collaborative sparsity. The present sources are identified by looking at the sub-dictionaries automatically selected in the coding. The collaborative filtering in C-HiLasso takes advantage of the temporal/spatial redundancy in the mixed signals, letting collections of samples collaborate in identifying the classes, while allowing in dividualsamples to have different internal sparse representations. This collaboration is critical to further stabilize the sparse representation of signals, in particular the class/sub-dictionary selection. The internal sparsity inside the sub-dictionaries, as naturally incorporated by the hierarchical aspects of C-HiLasso, is critical to make the model consistent with the essence of the sub-dictionaries that have been trained for sparse representation of each individual class. We present applications from speaker and instrument identification and texture separation. In the case of audio signals, we use sparse modeling to describe the short-term power spectrum envelopes of harmonic sounds. The proposed pitch independent method automatically detects the number of sources on a recording.

Automatic ultrasound (US) image segmentation is a difficult task due to the quantity of noise present in the images and the lack of information in several zones produced by the acquisition conditions. In this paper, we propose a method that combines shape priors and image information to achieve this task. In particular, we introduce knowledge about the rib-eye shape using a set of images manually segmented by experts. A method is proposed for the automatic segmentation of new samples in which a closed curve is fitted taking into account both the US image information and the geodesic distance between the evolving curve and the estimated mean rib-eye shape in a shape space. This method can be used to solve similar problems that arise when dealing with US images in other fields. The method was successfully tested over a database composed of 610 US images, for which we have the manual segmentations of two experts.

A method is presented for segmentation of anatomical structures that incorporates prior information about shape. The method iteratively applies steps, which find object's border considering its properties independently from shape. The boundary is regularized taking in account the shape being extracted. Detection is not directly performed in the image but in a "shape space" referred to the shape in each step. The problem is reduced to work in this new coordinate system where the border is approximately a horizontal line. Shape information is introduced through a higher dimensional map similar to a distance map of a mean shape. Segmentation results are demonstrated on ultrasound imagery to measure meat quality of bovine and ovine livestock.