Guitar and Bass are among the most popular stringed instruments in the world, being part of the typical rock-and-roll ensemble. However, distinguising between these two visually has been a problem for ages, specially for the general public. This has brought hate and laughs to the community in equal share.
Back in the day, bass and guitar differed in the number of strings they had, as well as the thickness of those...but with people adding crazy amount of strings to these two instruments, telling them apart is only getting more difficult to solve! This silly little project was created to explore CNNs for classification using a real-world training data, extracted from an actual musical instruments shop : Thomann.
This repo contains all the code for the web-scraping process, as well as the CNN training and inference.
This is a Convolutional Neural Network project carried out for the Automated Programming course of the MsC. in Computer Science and Techonology at Universidad Carlos III de Madrid The project consists mainly of two Google Colab Notebooks, one for the web-scraper and another for the CNN.
The scraper Thomann_Web_Scraping_coredamnwork.ipynb is probably deprecated by now just because of the changes that
that Thomann's website might have suffered.
This script makes use of the requests and BeautifulSoup libraries.
The resulting dataset contains 8000 images of bass and guitars (150x150 RGB, thumbnail-like). There is an imbalance
on the number of guitars (6924) vs bass images (1076).
The CNNs is implemented in Pytorch (see Bass&Guitar_Convolutional_Net_coredamnwork.ipynb) and everything was tested on
top of Google Colab/Drive File System.
You can find the full report of the project and the final presentation in the /docs folder (in Spanish though).
The complete dataset (obtained in 2019) as well as pre-trained models for inference can be downloaded here.
