Basic Time-Domain Features Extraction

[eryk-urbanski]

Creation of pipelines for different basic time domain features extraction. Possible parameterizations to implement:

• Amplitude Envelope (AE)
• Root Mean Square (RMS)
• Zero-crossing Rate (ZCR)
• Temporal Centroid (TC)

Steps:

1. Function for simple preprocessing using plap (read audio, framing, windowing)
2. Functions for calculating features (they should return the result)

In the next iteration the created functions will be rewritten in a way so they can be integrated with the existing plap code.

Guidelines:
Obviously these calculations mustn't use external libraries like Librosa. Only scipy and numpy can be used. Try prompting gpt4 for 'how to calculate feature name using an algorithm having already passed the input signal through block processing (framing) with overlapping and windowing' or something similar - experiment with prompts, because gpt may be helpful.
The results should be validated -> unit tests have to be written.