短時距傅立葉變換（Short-time Fourier Transform, STFT）是一種用 於時頻分析中的工具，可以描繪資料中頻域與時域的變化。其核心概念 為將長時間訊號根據設定之窗格大小切割為數個較短等長訊號，並將分 割出的訊號做傅立葉變換，以彌補傳統傅立葉變換無法描述時間變化的 劣勢。然而，窗格大小的設置會影響到頻率與時間的解析度折衷，且固 定窗格大小的情況不一定能適應資料中不同的頻率變化。本論文提出了 自適應窗格調整的方法，能夠根據資料的變動調整窗格大小，以符合該 資料區段最適合之頻率與時間解析度，解決上述所提及無法根據資料頻 率所變化的窗格大小問題。我們在四種資料集中比較並評估自適應窗格 之短時距傅立葉變換與固定窗格之短時距傅立葉變換的優劣。實驗結果 顯示，我們的方法能夠保持更為穩健的表現。;The Short-Time Fourier Transform (STFT) is a widely used tool for time-frequency analysis that captures variations in both frequency and time domains within a signal. The core concept involves segmenting a long-duration signal into multiple shorter, equal-length segments using a predefined window size, then applying the Fourier Transform to each seg- ment. This approach solves the limitation of traditional Fourier Trans- form, which lacks time localization. However, the choice of window size affects the trade-off between frequency and time resolution. A fixed win- dow size may not be suitable for signals with varying frequency charac- teristics. This paper proposes an adaptive window adjustment method that dynamically modifies the window size based on changes in the data, allowing for the most appropriate frequency and time resolution in each segment. To evaluate the effectiveness of the proposed method, we com- pare the adaptive-window STFT with traditional STFT across four differ- ent datasets. Experimental results demonstrate that our method delivers more robust performance.
