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Learning TranspositionInvariant Interval Features from Symbolic Music and Audio by Stefan Lattner
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Many music theoretical constructs (such as scale types, modes, cadences, and chord types) are defined in terms of pitch intervals---relative distances between pitches. Therefore, when computer models are employed in music tasks, it can be useful to operate on interval representations rather than on the raw musical surface. Moreover, interval representations are transposition-invariant, valuable for tasks like audio alignment, cover song detection and music structure analysis. We employ a gated autoencoder to learn fixed-length, invertible and transposition-invariant interval representations from polyphonic music in the symbolic domain and in audio. An unsupervised training method is proposed yielding an organization of intervals in the representation space which is musically plausible. Based on the representations, a transposition-invariant self-similarity matrix is constructed and used to determine repeated sections in symbolic music and in audio, yielding competitive results in the MIREX task Discovery of Repeated Themes and Sections . • Related links: • https://arxiv.org/pdf/1806.08236.pdf • https://shorturl.at/moYZ8 • Publications: • • Lattner, S., Grachten, M., and Widmer, G. Learning transposition-invariant interval features from symbolic music and audio. In Proceedings of the 19th International Society for Music Information Retrieval Conference, ISMIR 2018, Paris, France, September 23-27 • • Lattner, S., Grachten, M., and Widmer, G. A predictive model for music based on learned interval representations. In Proceedings of the 19th International Society for Music Information Retrieval Conference, ISMIR 2018, Paris, France, September 23-27 • Related Publications: • • Arzt, A. and Lattner, S. Audio-to-score alignment using transposition-invariant features. In Proceedings of the 19th International Society for Music Information Retrieval Conference, ISMIR 2018, Paris, France, September 23- 27, 2018 • • Lattner, S. and Grachten, M. Learning transformations of musical material using gated autoencoders. In Proceedings of the 2nd Conference on Computer Simulation of Musical Creativity, CSMC 2017, Milton Keynes, UK, September 11-13, 2017
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