From the main acoustic impairments present in full-duplex audio communication systems, acoustic echo is the one that contributes to quality degradation the most. Acoustic echo occurs when the sound produced from one or multiple loudspeakers ➊ is captured by the microphone ➌ of the very system, and is sent back to the far-end of origin. The goal of acoustic echo reduction is two-fold: to reduce the echo components ➊ in the microphone signal ➌ to the extent that they become inaudible, and to preserve the main near-end sounds ➋ captured by the microphone clean and clear. Even after decades of research and development in this problem, the reduction of acoustic echo has not matured at the pace than other audio technologies have.
In a virtual conference, both loudspeaker and microphone should be active all the time, allowing all parties to talk and listen at any time. However, in order for that high-quality audio conversation to be possible, the videoconferencing system must remove efficiently the loudspeaker audio that is captured by the microphone, hence transmitting the voice of the people in the room only.
Check below how our solution performs in this difficult acoustic environment, plagued with multiple long-lasting echoes, under a “stress test” with permanent double talk.
Experiments conducted obtained in a similar deployment and room as that shown in the picture. Use Google Chrome for better experience.
In hands-free phone calls inside the car, the partner’s voice played by the loudspeaker is picked up by the very microphone. In order to accomplish a high-quality conversation, the car audio system must remove efficiently that echo, so that only the voice of the driver (or passengers) go through to the other end. Our solution performs exceptionally in this changing acoustic environment.
In speech enhancement for mobile devices by means of digital noise cancellation, the front microphone (click 1), picks up the user’s voice as well as background noise, while the two back microphones (click 2) capture different spatial replicas of said background noise. Our technology is able to cancel largely the disturbing noise, letting the user’s voice stand out distortionless (click 3).
Experiment conducted on a simulated scenario. Use Google Chrome for better experience.
In order to detect voice commands from the desired sound person, the audio front-end of a voice-commanded device must remove the system’s audio as well as a nearby sound interferences, all captured by the microphone array.
In active noise cancellation (ANC) for earphones, the noise sound waves (click 1) are picked up by the external microphone (click 3), so that the music (click 2) plus the “anti-noise” are played together by the loudspeaker (click 4). The learning machine is controlled by the internal microphone (click 5), resulting in the acoustic cancellation of the noise within the ear canal. Our technology is capable of estimating the primary path (3-5), the secondary path (4-5) and the feedback path (4-5) simultaneously.
In summary, when the ANC if OFF the user hears both the music and the noise (click 4), and when the ANC is ON the user hears the music only (click 5).