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Abstract: Automatic birdsong recognition using prediction-based singleton-type recurrent neural fuzzy networks (SRNFNs) is proposed in this paper. The recognition task consists of two stages. The first stage segments a significant portion from a birdsong sequence and the second stage performs recognition. For birdsong segmentation, an easy but effective segmentation approach based on time domain energy is proposed. For recognition, the linear predictive coding (LPC) coefficients of each frame in a segmented birdsong are extracted and used as features. These features are fed as inputs to SRNFN recognizers. The SRNFN is constructed by recurrent fuzzy if–then rules with fuzzy singletons in the consequences, and its recurrent aspect makes it suitable for processing patterns with temporal characteristics. In birdsong recognition, the sample prediction technique is used, where one SRNFN is responsible for learning the temporal birdsong relationships of only one species. The prediction error of each SRNFN is then used as a criterion for recognition. Experiments with 10 species of birds and their songs are performed, and a high recognition rate is achieved. Comparisons with a Takagi–Sugeno–Kang (TSK)-type recurrent fuzzy network (TRFN) and backpropagation neural network are also made in the experiments. [Copyright &y& Elsevier]

Abstract: Several implementations of Feedforward Neural Networks have been reported in scientific papers. These implementations do not allow the direct use of off-line trained networks. Usually, the problem is the lower precision (compared to the software used for training) or modifications in the activation function. In the present work, a hardware solution called Artificial Neural Network Processor, using a FPGA, fits the requirements for a direct implementation of Feedforward Neural Networks, because of the high precision and accurate activation function that were obtained. The resulting hardware solution is tested with data from a real system to confirm that it can correctly implement the models prepared off-line with MATLAB. [Copyright &y& Elsevier]