https://github.com/baggepinnen/julia_examples
Various examples of machine learning and signal processing in Julia
https://github.com/baggepinnen/julia_examples
Last synced: 6 months ago
JSON representation
Various examples of machine learning and signal processing in Julia
- Host: GitHub
- URL: https://github.com/baggepinnen/julia_examples
- Owner: baggepinnen
- Created: 2020-02-21T05:18:07.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2022-11-26T07:25:46.000Z (almost 3 years ago)
- Last Synced: 2025-03-24T08:55:46.350Z (7 months ago)
- Language: Jupyter Notebook
- Size: 25.3 MB
- Stars: 9
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Julia Examples
This repository will contain various examples of machine learning and signal processing tasks in Julia
## Current contents
- [Robust estimation](https://nbviewer.jupyter.org/github/baggepinnen/julia_examples/blob/master/identification_robust.ipynb): filtering of audio signals with large impulsive noise. Estimation of linear models and robust spectral estimation.
- [Robust spectral-line tracing](https://nbviewer.jupyter.org/github/baggepinnen/julia_examples/blob/master/robust_chirp_tracing.ipynb): Tracing time-varying frequency components in the presence of large impulsive noise using linear time-varying models.
- [Audio categorization using LDA and bag-of-audio words](https://nbviewer.jupyter.org/github/baggepinnen/julia_examples/blob/master/audio_topics.ipynb): Categorizing audio samples based on their frequency contents.
- [Measure distance between spectrograms using Dynamic Time Warping and Optimal Transport](https://nbviewer.jupyter.org/github/baggepinnen/julia_examples/blob/master/frequency_warping.ipynb): In this example, we will explore Dynamic Time Warping (DTW) and see if we can combine it with optimal transport. The application is distance measurement between two spectrograms.
- [Measure distance between spectrograms using Dynamic Time Warping and Optimal Transport 2](https://nbviewer.jupyter.org/github/baggepinnen/julia_examples/blob/master/frequency_warping2.ipynb): A continuation of the previous entry where the distances are used more extensively for detection.
- [Fish detection](https://nbviewer.jupyter.org/github/baggepinnen/julia_examples/blob/master/fish_detection.ipynb): In this example we use both DTW and optimal transport to detect fish sounds.
- [British bird classification](https://nbviewer.jupyter.org/github/baggepinnen/julia_examples/blob/master/british_birds.ipynb): In this example we use distances and root embeddings to classify birds. The dataset can be found here https://www.kaggle.com/rtatman/british-birdsong-dataset?select=birdsong_metadata.csv