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License: MIT License
Python Library for Audio Parameterization
License: MIT License
Creation of pipelines for different basic time domain features extraction. Possible parameterizations to implement:
Steps:
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.
The MFCC extraction pipeline aims to develop a method for extractic Mel-Frequency Cepstral Coefficients from audio signals. It should be implemented as a modular component allowing easy integration of other cepstral features in the future. Unit tests should be conducted. Validation of output data should be conducted as a comparison with values obtained using existing solutions (a module for obtaining those values is connected with another Issue).
Add fundamental functionalities:
Creation of a module that allows the calculation of Mel-Frequency Cepstral Coefficients using existing open-source libraries. The libraries to use in order of importance: Librosa, Spafe, pyAudioAnalysis. The goal of this module is to allow our own implementation's results to be compared with values obtained from other libraries. This module should be written in a way so the values from different calculations are easily accessible. For example a function called mfcc_librosa returns a numpy array containing mfcc values. This module will be expanded in the future as we implement more features and so it should be constructed as modular and extendible component,
Steps:
Important!
Different libraries may perform mfcc calculation differently. They may take as input only a frame (a part of the whole signal) and some may take the whole signal. These details should be checked and documented in some way (i.e. comments in code), so it is much more clear on how the results should be compared with our own implementation.
Creation of a module that allows the calculation of LPC coefficients using existing open-source libraries. The libraries to use in order of importance: Librosa, Spafe. The goal of this module is to allow our own implementation's results to be compared with values obtained from other libraries. This module should be written in a way so the values from different calculations are easily accessible. For example a function called mfcc_librosa returns a numpy array containing mfcc values. This module will be expanded in the future as we implement more features and so it should be constructed as modular and extendible component,
Steps:
Important!
Different libraries may perform lpc calculation differently. They may take as input only a frame (a part of the whole signal) and some may take the whole signal or even something else. These details should be checked and documented in some way (i.e. comments in code), so it is much more clear on how the results should be compared with our own implementation.
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