A flexible and multipurpose bio-inspired hierarchical model for analyzing musical timbre is presented in this paper. Inspired by findings in the fields of neuroscience, computational neuroscience, and psychoacoustics, not only does the model extract spectral and temporal characteristics of a signal, but it also analyzes amplitude modulations on different timescales. It uses a cochlear filter bank to resolve the spectral components of a sound, lateral inhibition to enhance spectral resolution, and a modulation filter bank to extract the global temporal envelope and roughness of the sound from amplitude modulations. The model was evaluated in three applications. First, it was used to simulate subjective data from two roughness experiments. Second, it was used for musical instrument classification using the k-NN algorithm and a Bayesian network. Third, it was applied to find the features that characterize sounds whose timbres were labeled in an audiovisual experiment. The successful application of the proposed model in these diverse tasks revealed its potential in capturing timbral information.

Fair resource allocation is an important and challenging issue for many telecommunication service providers. Many researchers are investigating fast and efficient algorithms which can provide such fair rates in a distributed manner. It is well-known that, for any rate allocation algorithm, almost all of the bottlenecks occur in the access part of the network. In the current work, we use this inherent feature for designing hierarchical methods of resource allocation and then with the use of the fast Newton method and fuzzy algorithms, we improve the convergence speed of the algorithm in comparison with the conventional ones. An important feature of the proposed method in comparison with the previous fuzzy implementations is its distributed nature, because there is no need for feedback from the core network in developing the desired fuzzy method.