ADVAIT KARNATAK

LyricsAnalyzer is a powerful tool that leverages Natural Language Processing (NLP) to analyze song lyrics. The project focuses on extracting valuable insights from lyrics, including sentiment analysis, genre prediction, and thematic categorization. By applying machine learning algorithms, the tool can classify songs based on mood, style, and genre, making it ideal for music classification, recommendation systems, and music analysis projects.

Key Features:
Sentiment Analysis: Understand the emotional tone of the lyrics (positive, negative, neutral).
Genre Prediction: Classify songs into various genres based on their lyrics.
Theme Detection: Identify common themes like love, social issues, etc., from lyrics.
Data Visualization: Visualize analysis results through charts and graphs for easy interpretation.
The project is built using Python and libraries like NLTK, Scikit-learn, and TensorFlow. It combines the power of text processing and deep learning techniques to provide accurate and insightful predictions.

LyricsAnalyzer is ideal for music-related businesses, content creators, and researchers who need to gain deeper insights into song lyrics or develop recommendation systems based on lyrics.

As part of my work with InspireAI, I integrated a comprehensive end-to-end recommendation system designed to deliver personalized content based on users’ web-scraped LinkedIn profiles. The system was built to help users discover articles and insights aligned with their professional interests and career goals.

Key Contributions:
LinkedIn Profile Scraping: Developed a system to scrape and extract relevant data from LinkedIn profiles, ensuring rich user information for recommendations.
NLP Pre-processing & Word2Vec: Created a robust NLP pipeline to process user profile data, followed by Word2Vec vectorization for converting textual data into meaningful word embeddings.
Clustering & Recommendations: Applied K-Means clustering to categorize similar posts and articles, enabling the system to recommend personalized, contextually relevant content to users.

Key Features:
Personalized Content: Recommends articles, posts, and insights based on users’ professional background and interests.
Scalable Model: Built for scalability, capable of handling large datasets and evolving user preferences.

This recommendation engine effectively uses advanced NLP techniques and machine learning to enhance the user experience, providing tailored recommendations for career growth and industry insights.

AI-Powered Audio Analysis

This project focuses on accurately classifying songs into their respective genres using advanced Deep Learning and Audio Signal Processing techniques. It combines cutting-edge technology and creativity to deliver reliable music categorization, ideal for applications in music streaming, curation, and recommendation systems.

Key Highlights:

Audio Feature Extraction: Leveraged tools like Librosa to extract key audio features such as Mel spectrograms, MFCCs, and spectral contrast.
Deep Learning Models: Implemented CNNs ,RNNs and LSTMs to capture both spatial and sequential data in audio signals.
Multi-Genre Classification: Achieved high accuracy across multiple genres, ensuring scalability for large datasets.
Custom Pipelines: Built robust pre-processing pipelines for audio cleaning, normalization, and augmentation to enhance model performance.
Real-World Applications: Can be adapted for music discovery platforms, personalized playlists, and artist trend analysis.