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jameslyons avatar

jameslyons/python_speech_features

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2,422 stars·609 forks·Python·MIT·11 views

Python Speech Features

Python speech features is a signal processing toolkit and library for extracting standard speech recognition features from raw audio signals. It provides computational capabilities to calculate mel-frequency cepstral coefficients, raw and log filterbank energies, and spectral subband centroids for automated speech recognition systems and acoustic analysis.

The library implements audio signal transformations including pre-emphasis filtering, overlapping windowed frame segmentation, Fast Fourier Transform spectral analysis, mel-filterbank projection with configurable filter weights and frequency ranges, and discrete cosine transform cepstral projection.

Features

  • Audio Feature Extraction - Computes standard audio representations such as mel-frequency cepstral coefficients and filterbank energies from raw audio.
  • Mel-band Energy Extractions - Transforms audio frequency spectrums into perceptually-scaled filterbank energies using configurable mel-triangle filter weights.
  • Speech Recognition - Extracts standard audio representations like mel-frequency cepstral coefficients for speech recognition tasks.
  • Feature Extraction - Provides a signal processing toolkit for computing cepstral coefficients and filterbank energies for speech recognition.
  • Mel-Frequency Cepstral Coefficients - Applies a discrete cosine transform to convert log filterbank energies into cepstral coefficients.
  • Fourier Transforms - Converts windowed time-domain audio frames into frequency-domain power spectra using numerical Fourier transforms.
  • Spectral Centroids - Computes spectral subband centroids from audio signals to characterize frequency distribution.
  • Windowed Segmentation - Segments continuous audio waveforms into overlapping short-time frames using configurable window functions.
  • Audio Frequency Analysis - Analyzes audio signals by computing spectral subband centroids to characterize frequency distributions.
  • Pre-Emphasis Filters - Applies a high-pass digital filter to raw audio signals to amplify high frequencies.

Star history

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Frequently asked questions

What does jameslyons/python_speech_features do?

Python speech features is a signal processing toolkit and library for extracting standard speech recognition features from raw audio signals. It provides computational capabilities to calculate mel-frequency cepstral coefficients, raw and log filterbank energies, and spectral subband centroids for automated speech recognition systems and acoustic analysis.

What are the main features of jameslyons/python_speech_features?

The main features of jameslyons/python_speech_features are: Audio Feature Extraction, Mel-band Energy Extractions, Speech Recognition, Feature Extraction, Mel-Frequency Cepstral Coefficients, Fourier Transforms, Spectral Centroids, Windowed Segmentation.

What are some open-source alternatives to jameslyons/python_speech_features?

Open-source alternatives to jameslyons/python_speech_features include: aubio/aubio — Aubio is an audio analysis and digital signal processing library designed for music information retrieval. It provides… tyiannak/pyaudioanalysis — pyAudioAnalysis is a Python library and framework for audio signal processing and analysis. It provides tools for… accord-net/framework — This project is a scientific computing framework for the .NET ecosystem, providing a comprehensive suite of libraries… librosa/librosa — Librosa is a Python audio analysis library and digital signal processing framework. It functions as a feature… alphacep/vosk-api — Vosk is an offline speech-to-text engine and API that converts spoken audio into text locally on a device. It provides… allendowney/thinkdsp — ThinkDSP is a Python-based audio signal processing framework and educational resource designed for studying the…

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