论文标题
T-NGA：时间网络嫁接用于学习处理尖峰音频传感器事件的算法
T-NGA: Temporal Network Grafting Algorithm for Learning to Process Spiking Audio Sensor Events
论文作者
论文摘要
尖峰硅耳蜗传感器将声音编码为来自不同频率通道的异步流。缺乏用于尖峰耳蜗的标记培训数据集，因此很难在这些传感器的输出上训练深层神经网络。这项工作提出了一种称为“暂时网络移植算法（T-NGA）”的自我监管方法，该方法将在频谱图特征上培训的经常性网络移植，以便该网络可与耳蜗事件特征一起使用。 T-NGA训练仅需要时间对齐的音频谱图和事件功能。我们的实验表明，嫁接网络的准确性类似于使用软件尖峰耳蜗模型的事件从Scratch训练的监督网络的准确性。尽管尖峰硅耳蜗的电路非理想性，但使用N-TIDigits18数据集的硅耳蜗尖峰记录的移植网络精度仅比监督网络的准确性低约5％。 T-NGA可以在没有大标签的SPIKE数据集的情况下训练网络来处理尖峰音频传感器事件。
Spiking silicon cochlea sensors encode sound as an asynchronous stream of spikes from different frequency channels. The lack of labeled training datasets for spiking cochleas makes it difficult to train deep neural networks on the outputs of these sensors. This work proposes a self-supervised method called Temporal Network Grafting Algorithm (T-NGA), which grafts a recurrent network pretrained on spectrogram features so that the network works with the cochlea event features. T-NGA training requires only temporally aligned audio spectrograms and event features. Our experiments show that the accuracy of the grafted network was similar to the accuracy of a supervised network trained from scratch on a speech recognition task using events from a software spiking cochlea model. Despite the circuit non-idealities of the spiking silicon cochlea, the grafted network accuracy on the silicon cochlea spike recordings was only about 5% lower than the supervised network accuracy using the N-TIDIGITS18 dataset. T-NGA can train networks to process spiking audio sensor events in the absence of large labeled spike datasets.
