Overcoming Limitations of CNNs in Capturing Long-Range Dependencies in Ship-Radiated Noise

While Convolutional Neural Networks (CNNs) have demonstrated remarkable success in various domains, research suggests they may encounter challenges when dealing with long-range dependencies, particularly in the context of ship-radiated noise analysis. As highlighted in [10], CNNs could face difficulties effectively capturing these intricate relationships hidden within the acoustic data. This limitation arises from the inherent local receptive field of convolutional filters, which may hinder their capacity to model dependencies spanning extensive temporal ranges. Addressing this challenge necessitates exploring innovative approaches to enhance CNNs' ability to analyze ship-radiated noise effectively. Potential solutions involve incorporating mechanisms that extend the network's receptive field, enabling it to capture crucial long-range dependencies for more comprehensive and accurate acoustic analysis.