‘VoiceGate’: Accentize's Concept of Machine Learning Algorithm for Real-Time Noise Reduction
Based on machine learning techniques, ’VoiceGate’ smartly cleans up various noises from speeches and vocal recordings in real-time.
Recent advances in the area of machine learning have evidently shaped the way we handle, understand, and process data. New theoretical insights, the rise of freely available programming libraries, and the increasing access to computational resources provide a huge set of new possibilities to tackle data processing problems from a different angle. Accentize focuses on applying cutting-edge machine learning methods to the area of audio signal processing and closes the gap between state-of-the-art research and productive implementations. The newly released Accentize’s ‘VoiceGate' plug-in will help you to clean up almost all possible noises from your speech and vocal recordings in an easy and automated fashion.
‘VoiceGate’ is a real-time noise reducer working under specially trained artificial neural network that analyze human speech. The artificial neural network applied to ‘VoiceGate’ has been trained on more than 100 hours of audio data to learn the characteristics of human speech. With the help of machine learning techniques such as artificial neural networks, the algorithm of ‘VoiceGate’ can differentiate between desired signal components and unwanted noise which can be easily suppressed.
The VoiceGate Engine
‘VoiceGate’ is designed to detect general noises in two types, which are ‘Steady Noise’ and ‘Impulsive Noise’. Under this concept of noise detection, steady noise means a stationary and constant noise which does not fluctuate over time in its amplitude level such as white or pink noise. On the other hand, impulsive noise stands for a transient and instant noise which unexpectedly occurs over time such as a click or pop noise. Once the noise has been categorized into one of these two types, then ‘VoiceGate’ starts its attenuation processing against the noise over broad frequency range or only specific frequency region which users can define through the control panel of ‘VoiceGate’.
Controls Over Noises
Don’t forget the ‘VoiceGate’ is running under machine learning algorithm, so users do not really need to indulge in controlling parameters because ‘VoiceGate’ almost automatically implements controls over the noises. However, we need to briefly explore the functions and controls of ‘VoiceGate' in order to get better results from the machine learning mechanism of controlling noises.
The Sound from 'VoiceGate'
Under Broadband Mode, ‘VoiceGate’ reacted instantly against almost all types of noises from the vocal track recorded at a poorly treated room in acoustically. The Steady Noise filter automatically detected and removed intermittent birds singings coming through the window and the unknown hum noises. Meanwhile, the Impulsive Noise filter eliminated any sibilant and harsh breathing sounds from the vocal recorded with a low-cost microphone in real time. ‘VoiceGate’ removes noise in this way reproducing sounds more clear and definite, and the surprising thing is that the sound processed by VoiceGate has no distortion or exaggeration due to keeping the frequency areas of sounds other than noise intact. This means the filters do not affect the areas outside the noises.
Spectral Focus Mode enables surgical elimination of noises in a narrow frequency area, so I could remove only high-frequency sibilances from the vocal keeping the other noises still alive. Users can monitor noises from the selected region and this seems quite effective in finding target-noises to be removed or preserved.
The VoiceGate’s machine learning algorithm will be continuously improved by constantly adding new data from various user experiences into its artificial neural network, so ‘VoiceGate’ will handle almost all kind noises from human speeches and vocals.
With ‘VoiceGate’ You can do:
- Reduce noises from speech and vocal recordings in real-time
- Eliminate clicks and pops
- Eliminate background noises
- Tackle different kinds of noises without introducing many artifacts
- and more.
*Note that a 7-day, fully-functional trial version for macOS and Windows can be downloaded free: here
Accentize Recent advances in the area of machine learning have evidently shaped the way we handle, understand and process data. New theoretical insights, the rise of freely available programming libraries and the increasing access to computational ressources provide a huge set of new possibilities to tackle data processing problems from a different angle. We focus on applying cutting-edge machine learning methods to the area of audio signal processing and close the gap between state-of-the-art research and productive implementations.
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