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https://github.com/tadghw/pprrank
backend to a web app I built in Java from sockets, connected to a mongoDB database cluster to keep a list of commercially available community-recommended headphones and rank them by their Predicted Preference Rating. It's containerized with Docker and hosted on Google Cloud Run
https://github.com/tadghw/pprrank
audio java metrology mongo-db web-app
Last synced: 11 days ago
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backend to a web app I built in Java from sockets, connected to a mongoDB database cluster to keep a list of commercially available community-recommended headphones and rank them by their Predicted Preference Rating. It's containerized with Docker and hosted on Google Cloud Run
- Host: GitHub
- URL: https://github.com/tadghw/pprrank
- Owner: TadghW
- Created: 2022-11-22T22:53:02.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2023-01-21T15:03:27.000Z (almost 2 years ago)
- Last Synced: 2024-10-11T05:43:05.422Z (about 1 month ago)
- Topics: audio, java, metrology, mongo-db, web-app
- Language: Java
- Homepage: https://headphones.science
- Size: 91.8 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: readme.md
Awesome Lists containing this project
README
# Headphone Predicted Preference Rating List
Pierre Aubert's [Spinorama loudspeaker measurement collection list](https://pierreaubert.github.io/spinorama) is an outstanding community resource which presents a large collection of [CEA2034](https://webstore.ansi.org/Standards/CEA/cea20342015ansi) loudspeaker system measurements (spinoramas) and uses them to provide predicted preference ratings using Dr.Sean Olive's [Multiple Regression Model for Predicting Loudspeaker Preference Using Objective Measurements](https://www.aes.org/e-lib/browse.cfm?elib=12794).
Dr.Olive's predicted preference rating model for loudspeakers is a powerful implement with a near-perfect correlation to listener preference (r = 0.995). While this is an outstanding achievement and can be relied upon to provide tuning guidelines for speakers the same system is not predictive of listener preference for other audio devices like in-ear-monitors or headphones.
Headphones create a more complicated acoustic system than speakers to do due to the complex and differing acoustic properties of the human head, pinnae, and ear canal. As a consequence of that reality, systems for predicting the preference that listeners will show for different headphones have been slower to develop.
In 2018 Dr.Olive, MSc Todd Welti and BSc Omid Khonsari published a paper on their new [Statistical Model that Predicts Listener's Preference Ratings of Around-Ear and Over-Ear Headphones](https://www.aes.org/e-lib/browse.cfm?elib=19436). In that same paper they demonstrated that their model could predict listener preference near perfectly.
Despite the existence of a headphone predicted preference rating and application yet exists that catalogues headphone measurements from different sources and calculates their PPR in the same manner as Pierre Aubert's Spinorama list.
Developing a mono-dimensional list of headphones and their predictive preference scores is important as [there is no correlation between headphone price and performance](https://asa.scitation.org/doi/full/10.1121/1.4984044) and a near absence of resources which rigorously and quantitavely evaluate the performance of headphones.
This resource aims help fill that gap in the same manner as Mr.Aubert's Spinorama list. Please contact me at [email protected] for more information or if you want me to add a dataset to the list.
## Stuff I have yet to write:
1) Limitations of Existing Headphone Measurements
2) Limitations of Headphone PPR
3) Assessing the Quality of Headphone Measurements
4) Complications of different HRTFs in Preference