探索时频特征组合对水下声学信号识别效果的影响
Investigation of Time-Frequency Feature Combinations with Histogram Layer Time Delay Neural Networks
- 用直方图层时延神经网络处理多种时频特征组合
- 特定特征组合性能优于单一特征，显著提升识别准确率
- 适合从事水下声学信号处理的研究者参考
尽管深度学习减少了人工特征提取的需求，但通过特征工程对数据进行转换仍是提升模型性能的关键，尤其在水下声学信号领域。音频信号转化为时频表示的方式及其后续对频谱图的处理，会显著影响模型表现。本文研究了在直方图层时延神经网络中使用不同组合的时频特征所带来的性能差异。实验结果表明，某些特定的特征组合明显优于单一特征，从而确定了一组最优特征配置，为水下声学信号分析提供了有效方法。
原文摘要 · Abstract (English)
While deep learning has reduced the prevalence of manual feature extraction, transformation of data via feature engineering remains essential for improving model performance, particularly for underwater acoustic signals. The methods by which audio signals are converted into time-frequency representations and the subsequent handling of these spectrograms can significantly impact performance. This work demonstrates the performance impact of using different combinations of time-frequency features in a histogram layer time delay neural network. An optimal set of features is identified with results indicating that specific feature combinations outperform single data features.
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