Abstract
To make audio monitoring, the state of the art in this area makes use of local alignment algorithms between the objective audio and musical interpretation.The inductive hypothesis of a local alignment tool is that the alignment is correct to the current position of an error this is drag and accumulate to subsequent errors which do not recover unless elaborate heuristics are used. Our approach uses a local non-alignment scheme based on the audio search the entire purpose of short segments of audio taken from musical performance to get the k nearest audio segments (the proximity is determined using audio tracks based on entropy signs).The current audio segment of the play is paired with the nearest (in time) between the k previously selected audio segments of the target audio.To our knowledge, this is the first algorithm able to start up from an arbitrary point in the audio end, for example, if the musical performance had already begun when the monitoring system just went on.We complemented the overall strategy through a simple heuristic of ignoring the candidates when they are all too far in time with respect to the last position reported by the system.We have tested our method with 62 musical pieces, some of which are pop and classical music mostly.For every song we have two interpretations, we use one as the audio object and the other as the interpretation which will be monitored.We obtained excellent results.
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Acknowledgments
We thank the reviewers for their helpful suggestions and comments, and thank Dr. Grigori Sidorov for his help improving the wording of this article.
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An earlier version of this work was presented at the 9th Mexican International Conference on Artificial Intelligence (MICAI’2010) [8].
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Camarena-Ibarrola, A., Chávez, E. Online music tracking with global alignment. Int. J. Mach. Learn. & Cyber. 2, 147–156 (2011). https://doi.org/10.1007/s13042-011-0025-0
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DOI: https://doi.org/10.1007/s13042-011-0025-0