For audio search, the most suitable ANN (Approximate Nearest Neighbor) algorithms balance speed, accuracy, and scalability when matching high-dimensional audio embeddings. Hierarchical Navigable Small World (HNSW), Inverted File Index with Product Quantization (IVFPQ), and Locality-Sensitive Hashing (LSH) are widely used. These algorithms efficiently handle the dense vector representations generated from audio signals (e.g., via CNNs or transformers) while enabling fast similarity searches across large datasets.
HNSW is particularly effective for audio search due to its combination of high recall and low latency. It constructs a multi-layered graph where searches start at the top layer (coarse approximations) and refine results down to lower layers. This structure works well for audio embeddings, which often have complex spatial relationships. For example, Spotify’s audio recommendation system uses HNSW to match songs based on acoustic features. Its logarithmic scaling with dataset size also makes it practical for applications requiring real-time responses, such as identifying a song from a short audio clip. However, HNSW’s memory usage can be a limitation for extremely large datasets, requiring careful optimization.
IVFPQ and LSH are better suited for scenarios prioritizing memory efficiency or distributed processing. IVFPQ, implemented in libraries like FAISS, clusters embeddings into Voronoi cells (coarse quantizer) and compresses vectors using product quantization, reducing memory usage by up to 95%. This makes it ideal for billion-scale audio databases, such as Tencent’s music search engine. LSH, while less precise, provides a simple way to hash similar audio embeddings into the same buckets, enabling parallelizable searches. For example, Google’s AudioSet uses LSH-like techniques for rapid filtering before applying more precise matching. Developers often combine these algorithms—using LSH for initial candidate selection and HNSW/IVFPQ for refinement—to balance speed and accuracy in production systems.