‘DeRoom’: Accentize’s Intelligent Real-Time Reverb Reducer based on Machine Learning Techniques

’DeRoom’ removes unnecessary reverbs and room resonances by means of its internal machine learning algorithm in real-time.

The Music Telegraph | Text 2020/03/25 [10:10]

‘DeRoom’: Accentize’s Intelligent Real-Time Reverb Reducer based on Machine Learning Techniques

’DeRoom’ removes unnecessary reverbs and room resonances by means of its internal machine learning algorithm in real-time.

The Music Telegraph| 입력 : 2020/03/25 [10:10]

 

▲ DeRoom

© 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 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 ‘DeRoom’ plug-in will help you to reduce unnecessary reverbs and room resonances from audio recordings in an easy and automated fashion.

 

 

‘DeRoom’ is a real-time reverb reducer working under specially trained artificial neural network that analyze room acoustics. The artificial neural network applied to ‘DeRoom’ has been trained on many different room scenarios in order to be able to separate direct sound from reflection components. With the help of machine learning techniques such as the artificial neural network, ‘DeRoom’ can detect and remove reverbs and room resonances from any kind of room environments in real-time.

 

 

 

The DeRoom Engine

‘DeRoom’ is designed to detect and analyze reverbs and room resonances in three different simulated room types, which are Small, Medium, and Large. The algorithms for reverb detection have been developed in individual optimization sessions and can be easily compared against each other among these three types of room. Therefore, these three algorithms have been optimized for different scenarios of room-size. As a result, users can make fine-tune their results by selecting between the three different room types.

 

▲ Three algorithms are optimized for different scenarios of room size

 

 

 

 

Controls Over Reverbs

Don’t forget the ‘DeRoom’ is running under machine learning algorithm, so users do not really need to estimate and set room-timing constants by hand. The algorithm of ‘DeRoom’ will figure it out internally in an automatic fashion, so users only need to set the reverb reduction amount and let the neural network do the rest. 

 

 

▲ Room Size selection - small, medium, large


At the top of the plugin you will find the room size selection along with a bypass button. The room size can be set to either small, medium or large. Depending on the room scenario you are dealing with, selecting an appropriate room size will lead to the best effect possible. 

 

 

▲ Signal Level Visualizer


The signal level visualizer is being displayed at the center of the interface. This level visualizer shows the logarithmic signal energy of the audio signal being played. The gray part of the signal in the background shows the original level cut by filter while the colored part is the processed output signal. While playing the audio you can directly monitor how much signal is being taken away by the filter.

 

 

 

▲ Parameters for room-sound reduction


At the bottom of the interface you will find three different parameters to control room-sound reduction. The ‘Sensitivity’ defines how aggressive the reduction will operate on the signal. If users set to a low value Sensitivity then the target signal will be kept as smooth as possible but it will still contain some ‘room sound’ (reverbs and room resonances). Turning it up will lead to a better room-sound reduction, but the risk of arising artifacts may happen. 

 

The ‘Reduction’ parameter defines how much room-sound reduction you are aiming for. 100% Reduction will result in complete removal of the room sound and leave only the dry direct signal. 0% Reduction means no room-sound reduction at all. 

 

The ‘Make-up’ adds a broadband gain in order to compensate for possible level loss of the target signal after processing.

 

 

 

The Sound from 'DeRoom'

The drum sound from ‘DeRoom’ varies depending on the room-size settings and I realized the bigger the size the smaller the reverb tails. Each algorithm of ‘DeRoom’ automatically detects reverbs and room resonances based on the simulated room scenarios and this results in the different ratio between direct sound and reflections in each scenario. The filter cut the room sound in accurate and precise manner without touching other parts of the signals, so I could get clear direct sounds from the drum track without any modulation or coloration over the original signals. Again, the separation of direct sound from reflections is quite easy with 'DeRoom' plugin and it guarantees ultra reality in the processed sound. In addition, the ‘Sensitivity’ control can be used to change tonal quality of the target signal so users are able to shape the reflective walls as desired using this control. 

 

The artificial neural network applied to ‘DeRoom’ has been trained on many different room scenarios in order to be able to separate direct sound from reflection components, so ‘DeRoom’ plugin will handle almost all kind room sounds (reverbs and room resonances) from various room environments.  

 

 

 

 

 

With ‘DeRoom’ You can do:

 

- Reduce reverb and room resonances from audio recordings in real-time

- Separate direct sound from reflection components

- Shape room acoustics to your taste

- Tackle different kinds of reverbs without introducing many artifacts

- and more.

 

 

 

Price:

 

 

€41 EUR (Release discount until 27 March 2020)

 

 

*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.

 

 

 

 

 

 

For more information on 'DeRoom'

 

 

View this article in Korean version:  1   2

 

View this article in Japanese version:  1 

 

 

 

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