复杂值神经网络的高效设计,适用于短暂声信号的分类
Vlad S Paul1, Philip A Nelson1
1Institute of Sound and Vibration Research, University of Southampton, Southampton SO17 1BJ, United Kingdom.
The Journal of the Acoustical Society of America
|August 14, 2024
概括
单值分解 (SVD) 通过减少训练时间和提高声信号处理效率来增强复杂值的神经网络. 这种方法准确地分类了短暂的声学信号.
科学领域:
- 信号处理 信号处理
- 机器学习 机器学习
- 神经网络的神经网络的神经网络
背景情况:
- 复杂值的神经网络比实值网络具有优势,特别是在数据往往复杂的声信号处理中.
- 之前的工作证明了单值分解 (SVD) 用于修剪实值神经网络.
研究的目的:
- 研究SVD的应用,以缩短训练时间,提高复杂值神经网络中的执行效率.
- 为了证明基于SVD的修剪对于声信号分类的有效性.
主要方法:
- 复杂值多层感知子的矩阵形式的反向传播算法的导出.
- 应用SVD到复杂的重量矩阵用于网络修剪.
- 评估网络性能在短暂的声信号的分类.
主要成果:
- 基于SVD的修剪成功地减少了训练时间,并提高了复杂值神经网络的实现效率.
- 复杂值网络在所考虑的声处理任务中显示出性能优势.
- 用SVD修剪的网络实现了短暂声信号的准确分类.
结论:
- SVD提供了一种优化复杂值神经网络的有效方法,导致更快的训练和更高的效率.
- 用SVD优化的复杂值神经网络非常适合用于声信号处理任务.
- 基于SVD的修剪技术在分类复杂的声信号时保持高准确度.
相关概念视频
01:30Classification of Signals
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In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
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01:31Classification of Systems-II
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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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