Beat Tracking with a Cepstroid Invariant Neural Network
2016 (English)In: 17th International Society for Music Information Retrieval Conference (ISMIR 2016), International Society for Music Information Retrieval , 2016, 351-357 p.Conference paper (Refereed)
We present a novel rhythm tracking architecture that learns how to track tempo and beats through layered learning. A basic assumption of the system is that humans understand rhythm by letting salient periodicities in the music act as a framework, upon which the rhythmical structure is interpreted. Therefore, the system estimates the cepstroid (the most salient periodicity of the music), and uses a neural network that is invariant with regards to the cepstroid length. The input of the network consists mainly of features that capture onset characteristics along time, such as spectral differences. The invariant proper-ties of the network are achieved by subsampling the input vectors with a hop size derived from a musically relevant subdivision of the computed cepstroid of each song. The output is filtered to detect relevant periodicities and then used in conjunction with two additional networks, which estimates the speed and tempo of the music, to predict the final beat positions. We show that the architecture has a high performance on music with public annotations.
Place, publisher, year, edition, pages
International Society for Music Information Retrieval , 2016. 351-357 p.
Computer Science Other Computer and Information Science
Research subject Speech and Music Communication
IdentifiersURN: urn:nbn:se:kth:diva-195348OAI: oai:DiVA.org:kth-195348DiVA: diva2:1044322
17th International Society for Music Information Retrieval Conference (ISMIR 2016); New York City, USA, 7-11 August, 2016.
FunderSwedish Research Council, 2012 - 4685
QC 201611072016-11-022016-11-022016-11-14Bibliographically approved