supikiti/PNCC
A implementation of Power Normalized Cepstral Coefficients: PNCC
This tool helps researchers and engineers in speech processing analyze raw audio signals by converting them into Power Normalized Cepstral Coefficients (PNCCs). It takes an audio waveform as input and outputs a set of numerical features that represent the signal's phonetic content. Speech recognition scientists and audio engineers would use this to prepare audio for machine learning models or acoustic analysis.
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Use this if you need to extract robust, noise-resistant features from speech audio for tasks like speaker identification or speech recognition.
Not ideal if you need to analyze music, environmental sounds, or other non-speech audio, or if you prefer other feature extraction methods like MFCCs or filterbanks directly.
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54
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Language
Python
License
MIT
Category
Last pushed
Aug 11, 2019
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