Noise reduction using connectionist models

Using a back propagation network learning algorithm, a four-layered feed-forward network is trained on learning samples to realize a mapping from the set of noisy signals a set of noise-free signals. Computer experiments were carried out on 12 kHz sampled Japanese speech data, using stationary and nonstationary noise. The experiments showed that the network can indeed learn to perform noise reduction. Even for noisy speech signals that had not been part of the training data, the network successfully produced noise-suppressed output signals.<
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