Learn More
This paper presents the architecture and learning procedure underlying ANFIS (Adaptive-Network-based Fuzzy Inference System), a fuzzy inference system implemented in the framework of adaptive networks. By using a hybrid learning procedure, the proposed ANFIS can construct an input-output mapping based on both human knowledge (in the form of fuzzy if-then(More)
This book presents an engineering problem-driven approach to neural networks, fuzzy systems, and expert systems. The main goal of the book is to explain the principles of neural networks and fuzzy systems and to demonstrate how they can be applied to building knowledge-based systems for problem solving. To achieve this goal, the three main subjects of the(More)
A generalized control strategy that enhances fuzzy controllers with self-learning capability for achieving prescribed control objectives in a near-optimal manner is presented. This methodology, termed temporal backpropagation, is model-sensitive in the sense that it can deal with plants that can be represented in a piecewise-differentiable format, such as(More)
It is shown that, under some minor restrictions, the functional behavior of radial basis function networks (RBFNs) and that of fuzzy inference systems are actually equivalent. This functional equivalence makes it possible to apply what has been discovered (learning rule, representational power, etc.) for one of the models to the other, and vice versa. It is(More)
We present a quick and straightfoward way of input selection for neuro-fuzzy modeling using ANFIS. The method is tested on two real-world problems: the non-linear regression problem of automobile MPG (miles per gallon) prediction, and the nonlinear system identi-cation using the Box and Jenkins gas furnace data 1].
Compressive sampling (CS) is a new research topic in signal processing that has piqued the interest of a wide range of researchers in different fields recently. In this paper , we present a CS-based classifier for music genre classification , with two sets of features, including short-time and long-time features of audio music. The proposed clas-sifier(More)
Monaural singing voice separation is an extremely challenging problem. While efforts in pitch-based inference methods have led to considerable progress in voiced singing voice separation, little attention has been paid to the incapability of such methods to separate unvoiced singing voice due to its in harmonic structure and weaker energy. In this paper, we(More)
This paper describes a framework for modeling the machine transliteration problem. The parameters of the proposed model are automatically acquired through statistical learning from a bilingual proper name list. Unlike previous approaches, the model does not involve the use of either a pronunciation dictionary for converting source words into phonetic(More)
This paper proposes a novel and effective approach to extract the pitches of the singing voice from monaural polyphonic songs. The sinusoidal partials of the musical audio signals are first extracted. The Fourier transform is then applied to extract the vibrato/tremolo information of each partial. Some criteria based on this vibrato/tremolo information are(More)
Music genre classification is a challenging task in the field of music information retrieval. Existing approaches usually attempt to extract features only from acoustic aspect. However, spectrogram also provides useful information because it describes the temporal change of energy distribution over frequency bins. In this paper, we propose the use of Gabor(More)