Unsupervised learning techniques can enable audio search systems to identify patterns, group similar content, and retrieve relevant results without requiring labeled training data. These methods work by extracting meaningful features from raw audio, clustering or organizing data based on similarity, and enabling efficient similarity-based queries. This approach is particularly useful when dealing with large, unlabeled audio datasets where manual annotation would be impractical.
One key application is feature extraction using autoencoders or dimensionality reduction. For example, a convolutional autoencoder can process raw audio spectrograms to learn compressed representations (embeddings) that capture essential characteristics like pitch, rhythm, or timbre. These embeddings can then be indexed for fast similarity comparisons. Another example is using techniques like t-SNE or PCA to reduce high-dimensional Mel-frequency cepstral coefficients (MFCCs) into lower-dimensional spaces while preserving relationships between audio clips. This allows developers to compare audio files efficiently without relying on predefined labels or metadata.
Clustering algorithms like k-means or DBSCAN can group similar audio segments, enabling category-based search. For instance, a podcast platform might cluster episodes by topic using unsupervised topic modeling applied to speech-to-text transcripts. Even without transcriptions, methods like non-negative matrix factorization (NMF) applied to spectrograms can identify recurring acoustic patterns (e.g., identifying instrument types in music). For search implementation, developers can use approximate nearest neighbor libraries (e.g., FAISS) with unsupervised embeddings to quickly find audio matching a query clip. This combination of unsupervised feature learning and similarity search provides a flexible foundation for building audio search systems with minimal upfront labeling effort.