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Music Generation Via A Hidden Markov Model - Part 1
Manage episode 431690982 series 3474385
This story was originally published on HackerNoon at: https://hackernoon.com/music-generation-via-a-hidden-markov-model-part-1.
Automated music generation via a Hidden Markov Model (HMM).
Check more stories related to tech-stories at: https://hackernoon.com/c/tech-stories. You can also check exclusive content about #ai-music-generation, #ai-music, #generative-music, #sequence-models, #python-project, #python-guide, #sequence-to-sequence, #music-technology, and more.
This story was written by: @pictureinthenoise. Learn more about this writer by checking @pictureinthenoise's about page, and for more stories, please visit hackernoon.com.
This guide explains how to build a simple generative music application (that doesn't infringe on copyrighted music) using a Hidden Markov Model (HMM). You will build the application using a local Jupyter Notebook. A corpus of classical music will be used as input data and will serve as the basis for different experiments with the application.
301 episodios
Manage episode 431690982 series 3474385
This story was originally published on HackerNoon at: https://hackernoon.com/music-generation-via-a-hidden-markov-model-part-1.
Automated music generation via a Hidden Markov Model (HMM).
Check more stories related to tech-stories at: https://hackernoon.com/c/tech-stories. You can also check exclusive content about #ai-music-generation, #ai-music, #generative-music, #sequence-models, #python-project, #python-guide, #sequence-to-sequence, #music-technology, and more.
This story was written by: @pictureinthenoise. Learn more about this writer by checking @pictureinthenoise's about page, and for more stories, please visit hackernoon.com.
This guide explains how to build a simple generative music application (that doesn't infringe on copyrighted music) using a Hidden Markov Model (HMM). You will build the application using a local Jupyter Notebook. A corpus of classical music will be used as input data and will serve as the basis for different experiments with the application.
301 episodios
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