Update README.md

README.md

@@ -44,22 +44,6 @@ tags:
 	    - Cries for help
 	    - Normal indoor sounds
 
-    - Feature Extraction Process
-        1.	Audio Collection:
-            - Audio samples were sourced from datasets, such as AI Hub, to ensure coverage of diverse scenarios. 
-            - [AI Hub 위급상황 음성/음향](https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=&topMenu=&aihubDataSe=data&dataSetSn=170)
-            - These include emergency and non-emergency sounds to train the model for accurate classification.
-	    2.	MFCC Extraction:
-	        - The raw audio signals were processed to extract Mel-Frequency Cepstral Coefficients (MFCC).
-	        - The MFCC features effectively capture the frequency characteristics of the audio, making them suitable for sound classification tasks.
-            ![MFCC Output](./pics/mfcc-output.png)
-        3.	Output Format:
-            - The extracted MFCC features are saved as `13 x n` numpy arrays, where:
-            - 13: Represents the number of MFCC coefficients (features).
-            - n: Corresponds to the number of frames in the audio segment.
-        4.	Saved Dataset:
-            - The processed `13 x n` MFCC arrays are stored as `.npy` files, which serve as the direct input to the model.
-
 ### Model Description
 
 <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->