Fuzzy Content-Based Audio Retrieval Using Visualization Tools

Abstract: Music composition and sound design in the digital domain often involves sifting through large collections of audio files to find the right sample. Traditionally, this involves searching through metadata such as filenames and descriptors either via text search or by manually searching through folders. This paper presents a fast, scalable method for implementing a search engine in which the contents of audio files are used as queries to retrieve similar audio files. The presented approach applies visualization tools to speed up retrieval time compared to a simple KD-Tree algorithm. Qualitative and quantitative results are presented and benefits and drawbacks of the approach are discussed. While the qualitative results show promise, they are deemed inconclusive. Via the quantitative results, it is found that the application of UMAP yield an order-of-magnitude speed-up at a loss of accuracy and that the approach scales well with larger datasets.