The invention relates to the technical field of voice recognition, in particular to an implementation method and device of a multi-channel voiceprint
recognition system, and the implementation method comprises the steps of multi-
channel data acquisition and synchronization,
signal preprocessing and enhancement,
feature extraction and fusion, model training, real-time deployment and
adaptive optimization. Compared with the problems that a traditional multichannel voiceprint
recognition system depends on a
fixed beam forming
algorithm and an
independent clock synchronization module, the synchronization error is large, manual parameter adjustment is needed for
noise suppression, and generalization is poor, hardware-level
clock synchronization is achieved through a
PTP protocol, and the accuracy of
noise suppression is improved. The method combines an end-to-end neural network to automatically learn
noise distribution and a sound source space position, dynamically generates a beam forming weight, can improve the voice quality in a complex noise scene without manual intervention, remarkably reduces the interference of a synchronization error on sound source positioning, and enables the precision and stability of far-field voice enhancement to reach a new level.