利用松弛振荡神经元进行声源分离的耳蜗/AMtopic (CAM)和耳蜗/Spectrotopic (CSM)图谱

2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718) Pub Date : 1900-01-01 DOI:10.1109/NNSP.2003.1318065

R. Pichevar, J. Rouat

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引用次数: 10

摘要

我们使用一种双层无监督生物神经网络来分离声源，例如双元音或被非平稳噪声源入侵的元音。这个网络由尖峰神经元组成。两层的尖峰神经元由松弛振荡器模拟。网络的第一层是本地连接，第二层是全连接网络。为了正确分离声源，我们应该根据入侵声源的性质，使用耳蜗/声位图(CAM)或耳蜗/声位图(CSM)。

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Cochleotopic/AMtopic (CAM) and Cochleotopic/Spectrotopic (CSM) map based sound sourcce separation using relaxatio oscillatory neurons

We use a two-layered unsupervised bio-inspired neural network to segregate sound sources, e.g. double-vowels or vowels intruded by nonstationary noise sources. The network consists of spiking neurons. The spiking neurons in both layers are modeled by relaxation oscillators. The first layer of the network is locally connected, while the second layer is a fully connected network. We show that in order to correctly segregate sound sources, we should either use Cochleotopic/AMtopic map (CAM) or Cochleotopic/Spectrotopic map (CSM) depending on the nature of the intruding sound source.

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来源期刊

2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718)

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