Difference between revisions of "Music : Audio Analysis, Synchronization, And More"

From Tmplab
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== Audio Analysis Libraries  ==
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= Audio Analysis Libraries  =
  
 
=== PyAudio ===
 
=== PyAudio ===
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""Features""
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'''Features'''
 
* Extract audio features and representations (e.g. mfccs, spectrogram, chromagram)
 
* Extract audio features and representations (e.g. mfccs, spectrogram, chromagram)
 
* Classify unknown sounds
 
* Classify unknown sounds
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## musicinformationretrieval.com ##
 
## musicinformationretrieval.com ##
  
"" Links ""
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'''Links'''
 
* https://github.com/stevetjoa/musicinformationretrieval.com
 
* https://github.com/stevetjoa/musicinformationretrieval.com
  
 
Many examples about analyzing audio and doing Machine Learning with it
 
Many examples about analyzing audio and doing Machine Learning with it
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 +
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= Clock Synchronzation Libraries  =
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=== PyBeatSync ===
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PyBeatSync is a python project that uses the madmom audio and music analysis library to cary out beat detection from a live audio stream and generate a syncronised beat response.
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'''Links'''
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Source Code : https://github.com/louischaman/PyBeatSync

Revision as of 13:31, 12 June 2019

Audio Analysis Libraries

PyAudio

PyAudio provides Python bindings for PortAudio, the cross-platform audio I/O library. With PyAudio, you can easily use Python to play and record audio on a variety of platforms, such as GNU/Linux, Microsoft Windows, and Apple Mac OS X / macOS.

Links


Features

  • Read and play audio

Aubio

Links

aubio is a tool designed for the extraction of annotations from audio signals.

Features

  • segmenting a sound file before each of its attacks,
  • performing pitch detection,
  • tapping the beat
  • producing midi streams from live audio.


pyAudioAnalysis

Links

A Python library for audio feature extraction, classification, segmentation and applications


Features

  • Extract audio features and representations (e.g. mfccs, spectrogram, chromagram)
  • Classify unknown sounds
  • Train, parameter tune and evaluate classifiers of audio segments
  • Detect audio events and exclude silence periods from long recordings
  • Perform supervised segmentation (joint segmentation - classification)
  • Perform unsupervised segmentation (e.g. speaker diarization)
  • Extract audio thumbnails
  • Train and use audio regression models (example application: emotion recognition)
  • Apply dimensionality reduction to visualize audio data and content similarities

Examples and applied projects

    1. musicinformationretrieval.com ##


Links

Many examples about analyzing audio and doing Machine Learning with it


Clock Synchronzation Libraries

PyBeatSync

PyBeatSync is a python project that uses the madmom audio and music analysis library to cary out beat detection from a live audio stream and generate a syncronised beat response.

Links Source Code : https://github.com/louischaman/PyBeatSync