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• W2897070044 abstract "Speech recognition has become an important task to improve the human-machine interface. Taking into account the limitations of current automatic speech recognition systems, like non-real time cloud-based solutions or power demand, recent interest for neural networks and bio-inspired systems has motivated the implementation of new techniques. Among them, a combination of spiking neural networks and neuromorphic auditory sensors offer an alternative to carry out the human-like speech processing task. In this approach, a spiking convolutional neural network model was implemented, in which the weights of connections were calculated by training a convolutional neural network with specific activation functions, using firing rate-based static images with the spiking information obtained from a neuromorphic cochlea. The system was trained and tested with a large dataset that contains ”left” and ”right” speech commands, achieving 89.90% accuracy. A novel spiking neural network model has been proposed to adapt the network that has been trained with static images to a non-static processing approach, making it possible to classify audio signals and time series in real time." @default.
• W2897070044 created "2018-10-26" @default.
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• W2897070044 date "2018-07-01" @default.
• W2897070044 modified "2023-10-15" @default.
• W2897070044 title "Deep Spiking Neural Network model for time-variant signals classification: a real-time speech recognition approach" @default.
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• W2897070044 doi "https://doi.org/10.1109/ijcnn.2018.8489381" @default.
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