CALIBRATING NEURAL NETWORKS FOR SECONDARY RECORDING DEVICES Technical Report
CALIBRATING NEURAL NETWORKS FOR SECONDARY RECORDING DEVICES Technical Report
复制标题
校准辅助记录设备的神经网络技术报告
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Michal Kosmider
中科院分区：
文献类型：
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作者：
Michal Kosmider
本报告介绍了三星研发研究所波兰提出的DCASE 2019挑战任务1B的解决方案。任务1B的系统的主要焦点是一种新技术，旨在解决在目标辅助设备的示例有限的情况下从具有不同频率响应的麦克风学习的问题。这种技术独立于预测模型的架构，只需要几个例子就可以变得有效。
This report describes the solution to Task 1B of the DCASE 2019 challenge proposed by Samsung R&D Institute Poland. Primary focus of the system for task 1B was a novel technique designed to address issues with learning from microphones with different frequency responses in settings with limited examples for the targeted secondary devices. This technique is independent from the architecture of the predictive model and requires just a few examples to become effective.