Conversations in a vehicle can be eased significantly by an in-car communication system. Talking passengers are recorded by microphones, the speech-signals are enhanced, amplified and played back over loudspeakers to help the listeners.
Feedback cancellation
Feedback suppression
EQing strategies
Time-variant methods
Microphone combination techniques (beamforming, automatic SNR-based selection)
Noise reduction for stationary and instationary sources
Adaptive mixing and zoning
Automatic gain control
Voice-activity-dependent loss control
Noise-dependent gain control
Full-duplex communication
Loudspeaker and microphone equalization
Dynamics: Limiter, compressor, de-esser, etc.
Signal conditioning based on psychoacoustic criteria
Hands-free integration with echo cancellation and barge-in support
Development of customer-specific algorithms
Inject your own signal processing algorithms anywhere in our signal-chain for your custom demands.
ARM (Cortex-A series, Cortex-M series)
DSP (e.g. Analog Devices SHARC)
PC (Windows, Linux, macOS)
Your desired platform is not yet listed? Feel free to contact us and we will find a solution.
Our intercom system is the software solution for speech or audio requirements in your individual product – tailored to your needs.
In the operating room hitch-free communication is vitally important. However, there are numerous electrical devices that produce noise or behave as obstacles in the speech paths.
Our intercom system improves this situation by combining microphones, loudspeakers, and optimized algorithms to distribute speech and therefore provides the foundation for more efficient surgeries. The system can be integrated into medical devices or into the furnishing of the operating room.
Intercom is not restricted to medical environments. Generally, it simplifies speech communication in all environments that suffer from high-level background noise or moderately large distances between the interlocutors. Intercom addresses all these challenges and ensures hassle-free communication in environments, such as PA systems, conference rooms, audio/video chat, etc.
Feedback cancellation
Feedback suppression
EQing strategies
Time-variant methods
Microphone combination techniques (beamforming, automatic SNR-based selection)
Noise reduction for stationary and instationary sources
Cancellation of reference signals (text prompts, music, etc.)
Adaptive mixing and zoning
Automatic gain control
Voice-activity-dependent loss control
Noise-dependent gain control
Full-duplex communication
Loudspeaker and microphone equalization
Dynamics: Limiter, compressor, de-esser, etc.
Signal conditioning based on psychoacoustic criteria
Hands-free integration with echo cancellation and barge-in support
Development of customer-specific algorithms
Inject your own signal processing algorithms anywhere in our signal-chain for your custom demands.
ARM (Cortex-A series, Cortex-M series)
DSP (e.g. Analog Devices SHARC)
PC (Windows, Linux, macOS)
Your desired platform is not yet listed? Feel free to contact us and we will find a solution.
Our handsfree system currently comes in flavours optimized for cars, conferencing rooms, and heavy machines (agricultural and construction vehicles). It combines our core algorithms to deliver the best performance under your boundary conditions such as bandwidth, delay or resource restrictions.
Our goal is to integrate seamlessly into existing applications and exploit algorithmic synergies whenever possible. As an example, our in-car communication system features an integrated hands-free system with a combined feedback and echo canceller. This saves algorithmic complexity (no duplication of functionality) and even improves the cancellation performance in both applications.
A selection of featured sub-algorithms:
int handler(struct SonoEventSubscription *self, void* data, int code){
// React to the recognition event
fade_out_music_playback();
}
// Subscribe to the result changes in our real-time-audio suite
struct SonoEventSubscription *sr_result:
sonoEventSubscribeByPath(
sonoContext,
"~/core/srResult/result",
&handler,
NULL
);
This code can run alongside our real-time-audio suite in C. It uses the context-handle to our real-time-audio suite and allows you to react to any change in real-time on your processing device.
In this imaginary example it fades out the music playback.
int handler(struct SonoEventSubscription *self, void* data, int code){
// React to the recognition event
fade_out_music_playback();
}
// Subscribe to the result changes in our real-time-audio suite
struct SonoEventSubscription *sr_result:
sonoEventSubscribeByPath(
sonoContext,
"~/core/srResult/result",
&handler,
NULL
);
# Connect to device running sonoware real-time-audio suite
com: Communicator()
com.connect("10.42.8.21")
sr_result: com.register("~/core/srResult/result")
# Define handler that will turn on LEDs based on the wakeup-word
def turn_on_leds_on_wakeup_word(updated_model):
if updated_model.value:= 'hey sono':
turn_on_leds()
# Register that handler to be called each time there is a
# new speech-recognition result
sr_result.listen_on_change(turn_on_leds_on_wakeup_word)
This code runs on low-power embedded chips running micropython. Here we connect remotely to a sonoware real-time-audio suite and can react to any change in real-time.
In this example it toggles some LEDs, when the wakeup-word of the speech-recognition was uttered.
# Connect to device running sonoware real-time-audio suite
com: Communicator()
com.connect("10.42.8.21")
sr_result: com.register("~/core/srResult/result")
# Define handler that will turn on LEDs based on the wakeup-word
def turn_on_leds_on_wakeup_word(updated_model):
if updated_model.value:= 'hey sono':
turn_on_leds()
# Register that handler to be called each time there is a
# new speech-recognition result
sr_result.listen_on_change(turn_on_leds_on_wakeup_word)
The automatic detection of acoustic alarm signals of ambulances, police cars, or fire trucks is a vital element for improving our security in traffic. Human drivers can be warned early and autonomous vehicles also like to know what is happening around them. Subsequently emergency forces can reach their destination faster and safer.
We detect siren sounds automatically with a scalable approach depending on the needs of the application: From slim algorithms based on statistical models requiring only minimum processing resources up to large-scale Machine Learning solutions if accuracy matters.
You want to see our solution in action? No problem, we have you covered! Just hit the button and check out our online demos that showcase the performance of our algorithm in various conditions.
Support for different international siren sounds such as howling, wailing, Martinshorn.
Handles difficult scenarios such as Doppler shift, reverberation, and multiple sirens.
Microphone placement according to your specifications.
Estimate further event data, e.g. direction of the siren or relative speed.
Products with mechanical components are prone to wear, exterior influences, or errors in components or during assembly.
By using data from various sensors (microphones, accelerometers, e.g.) and latest Machine Learning algorithms even the smallest deviations in the operating state of a component can be detected. Continuously, periodically or just during production of your products – it’s up to you!
By condition based monitoring and predictive maintenance you are able to:
Support early on to find the best types of sensors and the best placement of the sensors to achieve a powerful cost-effective solution.
Gathering the right kind and amount of data is crucial. We support you in hitting the sweet spot.
We develop, train and test the Machine Learning models to fit the task.
Adjustments of the recognition models to the resources available.
