A scalable cover identification engine

Abstract

This paper describes the implementation of a content-based cover song identification system which has been released under an open source license. The system is centered around the Apache Lucene text search engine library, and proves how classic techniques derived from textual Information Retrieval, in particular the bag-of-words paradigm, can successfully be adapted to music identification. The paper focuses on extensive experimentation on the most influential system parameters, in order to find an optimal tradeoff between retrieval accuracy and speed of querying

Learning Low-Dimensional Embeddings of Audio Shingles for Cross-Version Retrieval of Classical Music

“…At the lower end of the specificity scale are tasks such as genre recognition [36]. A medium-level specificity is associated with tasks such as audio matching [12,14], version identification [31,37], live song detection [28,38], and cover song retrieval [8,10,13,15,16,[21][22][23][24][25][26][27]29,30]. In all of these tasks, one allows for variations as they typically occur in different performances and arrangements of a piece of music.…”

“…In the following, we analyze the influence of the parameter α, which is used in the loss function as defined in Equation (8). This parameter can be interpreted as the margin between d(x a , x p ) and d(x a , x n ).…”