In this paper, we present two methods based on neural networks for the automatic transcription of polyphonic piano music. The input to these methods consists in piano music recordings stored in WAV files, while the pitch of all the notes in the corresponding score forms the output. The aim of this work is to compare the accuracy achieved using a feedforward neural network, such as the MLP (Mult...

Continuous mapping models of spoken word recognition such as TRACE (McClelland and Elman, 1986) make robust predictions about a wide variety of phenomena. However, most of these models are interactive activation models with preset weights, and do not provide an account of learning. Simple recurrent networks (SRNs, e.g., Elman, 1990) are continuous mapping models that can process sequential patt...

This paper presents experimental comparison between selected neural architectures for chaotic time series prediction problem. Several feed-forward architectures (Multilayer Perceptrons) are compared with partially recurrent nets (Elman, extended Elman, and Jordan) based on convergence rate, prediction accuracy, training time requirements and stability of results. Results for chaotic logistic ma...

RNNs have local feedback loops within the network which allows them to shop earlier accessible patterns. This network can be educated with gradient descent back propagation and optimization technique such as second-order methods; conjugate gradient, quasi-Newton, Levenberg-Marquardt have also been used for networks training [14, 15]. But still this algorithm is not definite to find the global m...

High-dimensional large sample data sets, between feature variables and between samples, may cause some correlative or repetitive factors, occupy lots of storage space, and consume much computing time. Using the Elman neural network to deal with them, too many inputs will influence the operating efficiency and recognition accuracy; too many simultaneous training samples, as well as being not abl...

-. Port throughput forecast plays a great role in the development of port logistics economy. This paper works over that significant subject based on a new kind of nonlinear combination method, which is composed of linear exponential smoothing model, simple moving average method and Elman network. The forecasted values through the first two methods are taken as the inputs of Elman network, which...

This paper presents an intrusion detection system of hybrid neural network model based on RBF and Elman. It is used for anomaly detection and misuse detection. This model has the memory function .It can detect discrete and related aggressive behavior effectively. RBF network is a real-time pattern classifier, and Elman network achieves the memory ability for former event. Based on the hybrid mo...

Fixed points of recurrent neural networks can represent many things, including stored memories, solutions to optimization problems, and waypoints along non-fixed attractors. As such, they are relevant to a number of neurocomputational phenomena, ranging from low-level motor control and tool use to high-level problem solving and decision making. Therefore, global solution of the fixed point equa...

It is commonly assumed that innate linguistic constraints are necessary to learn a natural language, based on the apparent lack of explicit negative evidence provided to children and on Gold’s proof that, under assumptions of virtually arbitrary positive presentation, most interesting classes of languages are not learnable. However, Gold’s results do not apply under the rather common assumption...

of statistical learning: no need for algebraic rules, in Proc. 21st Annu. Conf. Cognit. Sci. Soc. and connectionism: analysis of a parallel distributed processing model of language We completely agree with Seidenberg and Elman 1 that what is important is 'understanding the behaviour of the network and the principles that govern it', but are mystified by their discussion of rules. Seidenberg and...