THE CONCEPT
This R&D initiative transforms massive, unstructured audio libraries into searchable, semantic data through automated high-fidelity tagging. The platform allows users to instantly navigate complex sonic landscapes, using vector-based similarity to bridge the gap between raw audio signals and human-readable metadata.
THE ENGINEERING
I architected a high-throughput pipeline that repurposes the ConvNeXt architecture to interpret the nuances of mel-spectrograms. By training the model on the extensive AudioSet corpus, the system generates robust, high-dimensional embeddings from 10-second acoustic windows. These embeddings are ingested into a dual-indexing OpenSearch environment, enabling sub-second k-nearest neighbor (k-NN) similarity searches across multi-terabyte datasets. The infrastructure utilizes Docker orchestration to ensure modularity, while a custom Streamlit engine provides a real-time visualization layer for spectrogram analysis and tag validation. This approach eliminates the manual annotation bottleneck, achieving precision in identifying complex instrumental patterns and spatial effects like reverb through a zero-shot extraction framework.
TECH STACK
Deep Learning: PyTorch, ConvNeXt (Audio-adapted), AudioSet.
Search & Indexing: OpenSearch (Vector Database), k-NN Search.
Processing: Mel-Spectrogram Transformation, Feature Embedding.
Interface: Streamlit, Librosa.
Infrastructure: Docker, Docker Compose, Python.