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.

翻译：尽管深度学习已减少了手动特征提取的普遍性，但通过特征工程进行数据转换对于提升模型性能仍然至关重要，尤其对于水下声学信号而言。音频信号转换为时频表示的方法以及后续对这些频谱图的处理方式会显著影响模型性能。本研究展示了在直方图层时延神经网络中使用不同时频特征组合对性能的影响。通过实验结果识别出一组最优特征集，表明特定特征组合的性能优于单一数据特征。