Fourier, Gabor, Morlet or Wigner: Comparison of Time-Frequency Transforms
원문 초록
In digital signal processing time-frequency transforms are used to analyze time-varying signals with respect to their spectral contents over time. Apart from the commonly used short-time Fourier transform, other methods exist in literature, such as the Wavelet, Stockwell or Wigner-Ville transform. Consequently, engineers working on digital signal processing tasks are often faced with the question which transform is appropriate for a specific application. To address this question, this paper first briefly introduces the different transforms. Then it compares them with respect to the achievable resolution in time and frequency and possible artifacts. Finally, the paper contains a gallery of time-frequency representations of numerous signals from different fields of applications to allow for visual comparison.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
VisionReasoner: Unified Visual Perception and Reasoning via Reinforcement Learning
Distributed LLMs and Multimodal Large Language Models: A Survey on Advances, Challenges, and Future Directions
AI-Driven Relocation Tracking in Dynamic Kitchen Environments
WalnutData: A UAV Remote Sensing Dataset of Green Walnuts and Model Evaluation
WARLearn: Weather-Adaptive Representation Learning
YCB-LUMA: YCB Object Dataset with Luminance Keying for Object Localization
토론
이 논문과 연결된 포럼 글이 아직 없습니다.