This document discusses using AI to appreciate Indian music by developing machine learning models that can analyze Indian music. It presents solutions that use chromagram, spectrogram, and Mel Frequency Cepstrum approaches to process music datasets in order to recognize musical patterns. The models are trained using transfer learning and evaluated. The system is containerized using Docker and deployed on Google Kubernetes Engine clusters that can be managed through the Google Cloud Platform console. A web application built with Django allows users to upload music files and see analysis results.

1. Appreciating
Indian Music
Using AI
:

2. Introduction

3. Problem Statement

4. Our Solution

5. Datasets

6. Data Processing

7. Chromagram approach
The term chroma feature or chromagram closely relates to the twelve
different pitch classes. Chroma-based features are a powerful tool for
analyzing music whose pitches can be meaningfully categorized.

8. Spectrogram approach
A spectrogram is a visual representation of the spectrum of frequencies of a
signal as it varies with time. When applied to an audio signal, spectrograms are
sometimes called sonographs, voiceprints, or voicegrams.

9. Mel Frequency Cepstrum (MFCC)
The MFCCs are a very powerful representation of an audio signal
as it scales the frequency in order to match more closely what the
human ear can hear.

10. Transfer Learning

11. Training

12. Model Evaluation

13. Tensorflow Extended

14. Docker

15. Continuous Integration (CI)

16. Kubernetes

17. List of Clusters

18. GCP Console

19. Web Application - Django

20. Upload Screen

21. Result Screen

22. Conclusion

23. Thank You