DeepLofargram: A Deep Learning based Fluctuating Dim Frequency Line Detection and Recovery
This addresses the challenge of detecting irregularly fluctuating frequency lines in signal processing, with incremental improvements in performance metrics.
The paper tackles the problem of detecting and recovering dim, fluctuating frequency lines in lofargrams, achieving an average performance boundary of -24dB and up to -26dB, which significantly improves state-of-the-art methods.
This paper investigates the problem of dim frequency line detection and recovery in the so-called lofargram. Theoretically, time integration long enough can always enhance the detection characteristic. But this does not hold for irregularly fluctuating lines. Deep learning has been shown to perform very well for sophisticated visual inference tasks. With the composition of multiple processing layers, very complex high level representation that amplify the important aspects of input while suppresses irrelevant variations can be learned. Hence we propose a new DeepLofargram, composed of deep convolutional neural network and its visualization counterpart. Plugging into specifically designed multi-task loss, an end-to-end training jointly learns to detect and recover the spatial location of potential lines. Leveraging on this deep architecture, the performance boundary is -24dB on average, and -26dB for some. This is far beyond the perception of human visual and significantly improves the state-of-the-art.