Audio similarity search allows for the retrieval of audio files that are similar to a given input, such as a song, audio clip, or sound pattern. The process involves converting audio into a mathematical representation, often through techniques like spectrograms or embeddings generated by deep learning models. These representations capture key features of the audio, such as tone, pitch, and rhythm.
Audio similarity search is used in applications such as music discovery, where users can find songs similar to one they enjoy, and in audio forensics, where matching audio recordings can be identified. Other use cases include podcast recommendation systems, sound effect matching, and audio content categorization. By using machine learning models to analyze and compare audio content, the system can efficiently identify relevant results based on a query’s audio characteristics.