Ruis Meter - Inleiding tot Ruis Onderdrukking
Na begrip de basis verschillen tussen ruis onderdrukking (onderdrukken luidspreker milieu lawaai ruis voor afstandsbediening luisteraars naar horen duidelijk) en actief ruis (offset de luisteraar % 27s eigen milieu lawaai ) % % ...
So, what if there only one microphone? If additional sound sources are not used for verification/comparison, a single microphone solution will rely on understanding the received noise characteristics and filtering them out. This is related to the previously mentioned definitions of steady-state and non-stationary noise. Steady state noise can be effectief filtered out through DSP algoritmes, while non-stationary noise poses a challenge, deep neural networks (DNNs) can help solve the problem.
Dit methode vereist a dataset voor training het netwerk. Dit dataset bestaat uit van verschillend (steady-state en niet-stationair) ruis en clear speech, creëren a gesynthetiseerd luidruchtig spraak patroon. Feed de dataset as input naar DNN en output it with clear voice. This will create a neural network model that will eliminate noise and only output clear speech.
Even met getraind DNN's, there zijn still some challenges and indicators to consider. If you want to run in real-time with low latency, you need strong processing power or a smaller DNN. The more parameters in DNN, the slower its running speed. The audio sampling rate has a similar effect on sound suppression. A higher sampling rate means that DNN needs to handle more parameters, but in turn, it will bereiken hogere kwaliteit output. Smalband spraak communicatie is an ideal keuze voor real-time ruis onderdrukking.
