Waveform Model

Waveform modeling focuses on representing and manipulating signals as continuous waveforms, aiming for accurate signal reconstruction, denoising, or generation. Current research emphasizes deep learning approaches, particularly transformer networks and convolutional neural networks (CNNs), often combined with other techniques like autoencoders and diffusion models, to address tasks such as seismic data reconstruction, speech enhancement, and music synthesis. These advancements improve the accuracy and efficiency of waveform processing across diverse fields, including medical signal analysis, audio processing, and geophysical data analysis, leading to more robust and informative results.