AudioComplete

Inspiration

The inspiration for AudioComplete came from a simple yet compelling idea: what if I could effortlessly generate background music tailored perfectly to vocal tracks? Recognizing the challenges musicians and content creators face in aligning vocals with fitting music, I envisioned the project to simplify and innovate the music creation process through intelligent automation.

What it does

AudioComplete automatically generates high-quality background music customized to any given vocal input. It extracts intricate audio features from the vocal track, classifies the genre accurately, and then utilizes a Variational Autoencoder (VAE) to subtly enhance the most similar musical tracks. The result is a harmonious and professionally synchronized piece, seamlessly blending vocals with complementary instrumental backgrounds.

How I built it

Building AudioComplete was a journey filled with creativity, problem-solving, and continuous learning. The core system revolves around audio feature extraction, CNN-based genre classification, and an optimized VAE for generating musical accompaniments. I carefully selected and processed reference audio tracks, performed sophisticated audio transformations, and meticulously tuned neural network architectures and hyperparameters.

Challenges I ran into

Training the Variational Autoencoder was particularly challenging and very time-consuming. Initial models produced distorted audio outputs and often failed to generate music matching the full duration of the vocals. The CNN's low confidence in genre detection prompted me to revisit and deeply analyze its internal linear algebra. Sitting down and working through the math from the ground up helped me understand where the model was lacking, eventually leading to a significant boost in performance. I also explored alternative approaches, like blending audio samples together without a VAE, but these methods resulted in unnatural transitions and inconsistent results.

Accomplishments that I'm proud of

I'm especially proud of overcoming the intricate challenges associated with training the VAE. Achieving smooth, artifact-free audio outputs and improving genre prediction accuracy after diving into the CNN’s fundamentals were major milestones. Turning early frustrations into major breakthroughs made the system more robust and intelligent.

What I learned

This project taught me invaluable lessons about audio signal processing, deep neural network architecture, and effective debugging strategies for complex AI systems. From handling distortions to improving model confidence and fine-tuning synchronization between tracks, I developed a deeper understanding of both the technical and creative sides of automated music production.

What's next for AudioComplete

Moving forward, I plan to fine-tune the VAE to produce even more coherent and stylistically refined outputs. I'll also expand support for additional genres, improve user customization, and explore real-time generation capabilities. Cloud-based collaboration tools and additional machine learning enhancements are also on the roadmap to further elevate the creativity and accessibility of AudioComplete.

Built With

• audio
• cnn
• dsp
• librosa
• pytorch
• vae