AI-trained noise suppression in the R7 now available!
AI-trained noise suppression is an advanced software algorithm that is embedded on all R7 portables with firmware R02.24.02.1002. The algorithm has been modeled to distinguish between the users' voice and surrounding noise such as vehicles, crowds and heavy machinery. This enables the radio to greatly enhance the clarity of the users' voice while suppressing any background noise - far beyond what enhanced noise suppression can do.
The radio also uses the Dual Input Noise Suppressor (DINC) which filters out noise based on the direction of the speech source rather than solely relying on noise patterns. The AI noise suppression then identifies the type of information in the audio content and processes it. Speech is retained while ambient noise is significantly reduced.
This feature can be configured in CPS/RM or via the menu (display model). Two additional Noise Suppressor options have been added in General Settings:
- AI-Trained Noise Suppressor Level 1. Artificial Intelligence trained noise reduction technology for advanced noise suppression of background noise. Level 1 allows some background noise to be heard for contextual awareness.
- AI-Trained Noise Suppressor Level 2. Artificial Intelligence trained noise reduction technology for advanced noise suppression of background noise. Level 2 minimizes background noise for clearer speech.
- Disabled. Disables all noise suppression.
- Basic. Basic noise suppression is applied. This results in about the same amount of noise suppression that was available in the first generation MOTOTRBO radios (e.g. DP3601).
- Enhanced Auto. Noise suppression is applied dynamically. In quiet environments, the noise suppression is reduced so that the audio is more natural sounding. In noisy environments, higher noise suppression is applied.
This feature also works with all R7 Series audio accessories such as remote speaker microphones - though most of these do not have do not have a dual microphone.
In this case, hearing is believing, so here are two audio samples to listen to:
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