  We present in this paper a new approach for polyphonic music transcription
using evolution strategies (ES). Automatic music transcription is a complex
process that still remains an open challenge. Using an audio signal to be
transcribed as target for our ES, information needed to generate a MIDI file
can be extracted from this latter one. Many techniques presented in the
literature at present exist and a few of them have applied evolutionary
algorithms to address this problem in the context of considering it as a search
space problem. However, ES have never been applied until now. The experiments
showed that by using these machines learning tools, some shortcomings presented
by other evolutionary algorithms based approaches for transcription can be
solved. They include the computation cost and the time for convergence. As
evolution strategies use self-adapting parameters, we show in this paper that
by correctly tuning the value of its strategy parameter that controls the
standard deviation, a fast convergence can be triggered toward the optima,
which from the results performs the transcription of the music with good
accuracy and in a short time. In the same context, the computation task is
tackled using parallelization techniques thus reducing the computation time and
the transcription time in the overall.
