Music genre classification based on dynamical models

This paper studies several alternatives to extract dynamical features from hidden Markov Models (HMMs) that are meaningful for music genre supervised classification. Songs are modelled using a three scale approach: a first stage of short term (milliseconds) features, followed by two layers of dynamical models: a multivariate AR that provides mid term (seconds) features for each song followed by an HMM stage that captures long term (song) features shared among similar songs. We study from an empirical point of view which features are relevant for the genre classification task. Experiments on a database including pieces of heavy metal, punk, classical and reggae music illustrate the advantages of each set of features

Adjustment of combination weights over adaptive diffusion networks

We show how the convergence time of an adaptive network can be estimated in a distributed manner by the agents. Using this procedure, we propose a distributed mechanism for the nodes to switch from using fixed doubly-stochastic combination weights to adaptive combination weights. By doing so, and by knowing when to switch, the agents are able to enhance their steady-state mean-square-error performance without degrading the rate of convergence during the transient phase of the learning algorithm

A new least-squares adaptation scheme for the affine combination of two adaptive filters

Adaptive combinations of adaptive filters are an efficient approach to alleviate the different tradeoffs to which adaptive filters are subject. rrhe basic idea is to mix the outputs of two adaptive filters with complementar~Tcapabilities, so that the combination is able to retain the best properties of each component. In previous works, we proposed to use a convex combination, applying weights.A(n) and 1-.A(n), with A(n) E (0,1), to the filter components, where the mixing parameter.A(n) was updated to minimize the overall square error using stochastic gradient descent rules. In this paper, we present a new adaptation scheme for.A(n) based on the solution to a least-squares (L8) problem, where the mixing parameter is allowed to lie outside range [0, 1]. Such affine combinations have recently been shown to provide additional gains. Unlike some previous proposals, the new L8 cOlnbination scheme does not require any explicit knowledge about the component filters. The ability of the L8 scheme to achieve optimal values ofthe mixing parameter is illustrated with several experiments in both stationary and tracking situations. Index Terms-.Adaptive filters, combination of filters

Functional Link Adaptive Filters for Nonlinear Acoustic Echo Cancellation

This paper introduces a new class of nonlinear adaptive filters, whose structure is based on Hammerstein model. Such filters derive from the functional link adaptive filter (FLAF) model, defined by a nonlinear input expansion, which enhances the representation of the input signal through a projection in a higher dimensional space, and a subsequent adaptive filtering. In particular, two robust FLAF-based architectures are proposed and designed ad hoc to tackle nonlinearities in acoustic echo cancellation (AEC). The simplest architecture is the split FLAF, which separates the adaptation of linear and nonlinear elements using two different adaptive filters in parallel. In this way, the architecture can accomplish distinctly at best the linear and the nonlinear modeling. Moreover, in order to give robustness against different degrees of nonlinearity, a collaborative FLAF is proposed based on the adaptive combination of filters. Such architecture allows to achieve the best performance regardless of the nonlinearity degree in the echo path. Experimental results show the effectiveness of the proposed FLAF-based architectures in nonlinear AEC scenarios, thus resulting an important solution to the modeling of nonlinear acoustic channels

A nonlinear architecture involving a combination of proportionate functional link adaptive filters

In this paper, we consider a functional link-based architecture that separates the linear and nonlinear filterings and exploits any sparse representation of functional links. We focus our attention on the nonlinear path in order to improve the modeling performance of the overall architecture. To this end, we propose a new scheme that involves the adaptive combination of filters downstream of the nonlinear expansion. This combination enhances the sparse representation of functional links according to how much distorted the input signal is, thus improving the nonlinear modeling performance in case of time-varying nonlinear systems. Experimental results show the performance improvement produced by the proposed model

Nonlinear Acoustic Echo Cancellation Based on Sparse Functional Link Representations

Recently, a new class of nonlinear adaptive filtering architectures has been introduced based on the functional link adaptive filter (FLAF) model. Here we focus specifically on the split FLAF (SFLAF) architecture, which separates the adaptation of linear and nonlinear coefficients using two different adaptive filters in parallel. This property makes the SFLAF a well-suited method for problems like nonlinear acoustic echo cancellation (NAEC), in which the separation of filtering tasks brings some performance improvement. Although flexibility is one of the main features of the SFLAF, some problem may occur when the nonlinearity degree of the input signal is not known a priori. This implies a non-optimal choice of the number of coefficients to be adapted in the nonlinear path of the SFLAF. In order to tackle this problem, we propose a proportionate FLAF (PFLAF), which is based on sparse representations of functional links, thus giving less importance to those coefficients that do not actively contribute to the nonlinear modeling. Experimental results show that the proposed PFLAF achieves performance improvement with respect to the SFLAF in several nonlinear scenarios

Adaptive Diffusion Schemes for Heterogeneous Networks

In this paper, we deal with distributed estimation problems in diffusion networks with heterogeneous nodes, i.e., nodes that either implement different adaptive rules or differ in some other aspect such as the filter structure or length, or step size. Although such heterogeneous networks have been considered from the first works on diffusion networks, obtaining practical and robust schemes to adaptively adjust the combiners in different scenarios is still an open problem. In this paper, we study a diffusion strategy specially designed and suited to heterogeneous networks. Our approach is based on two key ingredients: 1) the adaptation and combination phases are completely decoupled, so that network nodes keep purely local estimations at all times; and 2) combiners are adapted to minimize estimates of the network mean-square-error. Our scheme is compared with the standard Adapt-then-Combine scheme and theoretically analyzed using energy conservation arguments. Several experiments involving networks with heterogeneous nodes show that the proposed decoupled Adapt-then-Combine approach with adaptive combiners outperforms other state-of-the-art techniques, becoming a competitive approach in these scenarios.Comment: To appear in in IEEE Transactions on Signal Processing. URL: http://ieeexplore.ieee.org/document/8010454

Combinations of adaptive filters: performance and convergence properties

Adaptive filters are at the core of many signal processing applications, ranging from acoustic noise supression to echo cancelation [1], array beamforming [2], channel equalization [3], to more recent sensor network applications in surveillance, target localization, and tracking. A trending approach in this direction is to recur to in-network distributed processing in which individual nodes implement adaptation rules and diffuse their estimation to the network [4], [5]