Conclusion¶
Congratulations! You finished the book, executed every code we typed, and read every line we wrote!
In the first chapter, The Basics, we defined music classification and introduced its applications. We then looked into input representations with a special focus on biological plausibility. We also looked into music classification datasets with a special focus on the secrets of how to use some popular datasets correctly. In the evaluation section, we showed the concepts of important metrics such as precision and recall as well as code demo to compute them. After finishing this chapter, we hope you’re ready to start working on your music classification model.
In the second chapter, Supervised Learning, we reviewed popular architectures - their definitions, pros, and cons. We also demonstrated data augmentation methods for music audio - the code, spectrograms, and audio signals you can play. At the end of the chapter, we showed a full example of data preparation, model training, and evaluation on Pytorch. After this chapter, you can implement a majority of music classification models that were introduced during the deep learning era.
In the third chapter, Semi-Supervised Learning, we covered transfer learning and semi-supervised learning – approaches that became popular, recently, due to annotation cost. Both are strategies one can consider when there is only a small number of labeled items. These approaches can be useful in many real-world situations where you only have, for example, less than a thousand labeled items.
In the fourth chapter, Self-Supervised Learning, an even more radical approach. The goal of self-supervised learning is to learn useful representations without any labels. To achieve the goal, researchers assume some structural/internal patterns purely within input and design loss functions to predict the patterns. We covered a wide range of self-supervised learning methods introduced in music, speech, and computer vision. The lesson of this chapter liberates you from the worry of getting annotations.
In the fifth chapter, Towards Real-world Applications, we introduce you to what people care about in industry. After finishing this chapter, you can understand the procedures and tasks researchers and engineers in industry spend time on.
We’re delighted that you have studied music classification with us. Did you achieve your goal while reading it? Are your questions solved now? We hope we also achieved our goals - lowering the barrier of music classification to the newcomers, providing methods to cope with data issues, and narrowing the gap between academia and industry. Please feel free to reach out to us if you have any questions or feedback.
Best wishes,
Minz, Janne, and Keunwoo.