Title: Classifying 10 Common Music Genres with Deep Neural Networks Who: Sasha Liu sliu116, Julie Karam jkaram, Nathan DePiero ndepiero, Gavin Sabalewski gsabalew
Introduction: We will be implementing an existing paper titled, “Music Genre Classification using Machine Learning Techniques (Bahuleyan 2018). The paper’s objective was to create a music classification model based on genre for the purpose of automatic organization of music libraries - “Being able to automatically classify and provide tags to the music present in a user’s library, based on genre, would be beneficial for audio streaming services such as Spotify and iTunes” (Bahuleyan 2018). The study utilized the Audio set data set, and they report an AUC value of 0.894 and a testing accuracy of 0.65 for an ensemble classifier which combines the two proposed approaches. We aim to implement this model on another audio dataset and achieve similar performance.
Challenges: What has been the hardest part of the project you’ve encountered so far? We have encountered several challenges while working on our project. Our first challenge was becoming familiar with this new dataset. Audio files are something that we have not worked with before, so we spent a lot of time becoming familiar with this new data type. We also had to become familiar with a new analysis package, Librosa, which is a python package for music and audio analysis. It provides the building blocks necessary to create music information retrieval systems. Converting our audio files into spectrogram images which could then be turned into Tensors was a challenging task that we spent a lot of time on. Additionally, we are implementing our project in Pytorch, which none of us had prior experience with. We are familiarizing ourselves with this package, and applying our knowledge and skills from class to successfully implement DL models in Pytorch. Lastly, figuring out the best way to code collaboratively was a challenge that we encountered.
Insights: Are there any concrete results you can show at this point? We have our spectrogram images to show at this point. This is a major milestone that we have reached. We will soon have model performance metrics to show as well. We hope that it will perform as well as predicted.
Plan: Are you on track with your project? We are on track with our project. We have performed most of the preprocessing of our data and are ready to begin building and running our models. We will need to dedicate some more time to these tasks, but we believe that this will be pretty straightforward now that our data is ready.
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