Sonic Siren

Inspiration

Love for music: We're passionate about discovering new music and sharing it with others. Frustration with current recommendation systems: Many existing tools rely on basic genre or artist matching, often leading to predictable or repetitive results. Vision for a more personalized experience: We believe music recommendations should capture the unique essence of each individual's taste, going beyond surface-level similarities. Harnessing the power of AI: We saw an opportunity to leverage the capabilities of large language models to unlock deeper connections between songs and create more meaningful recommendations.

What it does

Takes two song names as input: Users provide two songs they already enjoy.
Generates a similar song recommendation: The AI model analyzes the musical qualities of the input songs and suggests a new song that shares similar elements, considering genre, tempo, mood, instrumentation, and overall sound.
Incorporates language preference: Users can specify a preferred language for the recommended song, ensuring alignment with their listening habits.
Provides explanation for recommendation: The model offers a brief explanation of why it chose the recommended song, fostering trust and understanding in the process.

How we built it

Technology stack:
    Python programming language
    Flask web framework
    Taipy GUI library
    Google's generative AI model (Palm)
    dotenv for environment variable management
    PIL for image processing
Key steps:
    Designed the user interface with Taipy.
    Integrated the Palm library for generating song recommendation.
    Implemented logic to handle user input and display recommendation.

Challenges we ran into

Fine-tuning AI recommendations: Balancing creativity and accuracy in the model's output.
Creating UI that user feel comfortable to use: Finding a simple yet attractive look.
Optimizing user experience: Ensuring a smooth and intuitive interface for users.

Accomplishments that we're proud of

Unique approach to music recommendation: Utilizing AI to explore deeper musical connections.
User-friendly interface: Providing a simple and engaging way to discover new music.
Language preference feature: Enhancing personalization for diverse music tastes.
Explanatory recommendations: Building trust and transparency in the model's decision-making.

What we learned

Collaboration and problem-solving: Working effectively as a team to overcome technical challenges.
AI model integration: Understanding the nuances of working with generative AI models.
User-centered design: Prioritizing user experience in every step of development.
Importance of feedback: Gathering feedback to iterate and improve the tool.

What's next for Sonic Siren

Incorporating user feedback: Refining the tool based on user suggestions and preferences.
Exploring additional features: Offering music discovery based on mood, activities, or other contextual factors.
Potential commercialization: Exploring possibilities for broader distribution and use.

Built With

• taipy