Music-Graph2Vec: An Efficient Method for Embedding Pitch Segment
Article No.: 97, Pages 1 - 5
Abstract
Learning low-dimensional continuous vector representation for short pitch segment extracted from songs is has been confirmed to contain tonal features of music, which is key to melody modeling that can be utilized in many music investigations, such as genre classification, emotion classification, and music retrieval, and so on. The skip-gram version of Word2Vec is ubiquitous, and widely used approach for music pitch segment embedding, but it poorly scales to large data sets due to its extremely long training time. In this paper, we propose a novel efficient graph-based embedding method, named Music-Graph2Vec, to tackle this concern. This approach converts music files into graphs, extracts the rhythmic sequence through random walking, and trains the rhythmic embedding model using skip-gram. Experimental results demonstrate that Music-Graph2Vec outperforms Word2Vec in training rhythmic embedding, with the advantage of being 55 times faster on the top-MAGD dataset (2,134.7s for Word2Vec and 38.9s for Music-Graph2Vec), with the same accuracy for Word2Vec in terms of music genre classification.
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Index Terms
- Music-Graph2Vec: An Efficient Method for Embedding Pitch Segment
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Published In
December 2023
745 pages
ISBN:9798400702051
DOI:10.1145/3595916
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Published: 01 January 2024
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