Online music tracking with global alignment

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.Keywords Follow-up Á Music Á Entropy Á Index Á Near GroundsReal-time tracking of musical performances consists in setting the position on the target audio segment most recently captured audio of a musical performance as this is being played. Now explain briefly three applications of real-time tracking of musical performances: (a) The virtual music teacher, (b) auto-accompaniment of soloists, and (c) automatically added special effects in live performance. For the virtual master of music, the sound objective is wellexecuted piece of music by a professional musician. Musical performance which will be followed is that produced by the student. A music teacher teaches only one student at a time, as the student plays his instrument, the teacher makes comments pointing out errors or endorse the interpretation. A virtual teacher should do the same, use the audio signal generated by the student, and processed in real time and provide appropriate instructions to the student. The advantage of a virtual teacher that can generate many instances of the same and therefore simultaneously serve many students. For the automatic accompaniment of a singer or a solo audio music performance, the target audio would be made possibly in a recording studio, while the music that will be followed up is the live performance of the musician or singer. The system plays music accompaniment while generating activities (e.g., introducing delays in background music), these systems are true An earlier version of this work was presented at

On the Use of Locality Sensitive Hashing for Audio Following

“…In Score Following (SF) the audio signal is aligned in realtime to a symbolic music score, this audio to score alignment is a well studied problem in which mainly two approaches have been used: dynamic programming [1], [2], [3], [4], [5], [6], [7] and stochastic models, usually Hidden Markov Models (HMM's) [8], [9], [10], [11], [12], [13]. Online music tracking systems were also presented in [14] and [15].…”