Rapid solution for searching similar audio items
This addresses the challenge of efficient audio similarity search for applications dealing with large datasets, but it appears incremental as it applies existing hashing methods to audio data.
The paper tackles the problem of high computational complexity in searching for similar audio items by using Locality Sensitive Hashing and feature selection based on sound production principles to down-sample feature vectors, resulting in faster search processes.
A naive approach for finding similar audio items would be to compare each entry from the feature vector of the test example with each feature vector of the candidates in a k-nearest neighbors fashion. There are already two problems with this approach: audio signals are represented by high dimensional vectors and the number of candidates can be very large - think thousands. The search process would have a high complexity. Our paper will treat this problem through hashing methodologies more specifically the Locality Sensitive Hashing. This project will be in the spirit of classification and clustering problems. The computer sound production principles will be used to determine which features that describe an audio signal are the most useful. That will down-sample the size of the feature vectors and speed up the process subsequently.