PureAudio-AI DA-250Q OEM Array Microphone-Speakerphone SystemCompact Digital Stereo Array Microphone with beam forming, noise reduction and full duplex acoustic echo cancelation functionality.
The DA-250Q array microphone-speakerphone system provides superior performance of AI NLP conversational computing, enabling touchless applications in devices such as: Automated Teller Machines with remote video teller, QSR Drive-Thru systems with AI voice order entry, Interactive Kiosks with information search, Building Entrance Intercom systems with virtual doorman/concierge services, Autonomous Automobiles with voice control, and Smart Vending Machines among others.
Andrea Electronics is a certified OEM microphone technology partner for IBM Watson™ Assistant Solutions
Andrea array microphone technology improves Watson speech recognition word error rate accuracy by almost 50% over competitive far-field noise canceling microphones when used in real world noisy environments.
The DA-250Q is a compact Stereo Array Microphone Digital Signal Processor (DSP) circuit board platform that can easily be integrated into many different OEM devices that would greatly benefit from directional noise canceling microphone performance and full duplex acoustic echo cancelation for speakerphone functionality.
Requiring only two tiny holes in the faceplate of a device, the DA-250Q OEM Array Microphone can deliver directional microphone sensitivity without the need for acoustic baffling, as found in large parabolic waveguides or long shotgun tube type microphones.
The DA-250Q is perfect for systems that demand superior microphone sound quality and performance for speech recognition accuracy and/or communication system intelligibility, while at the same time requiring the user to speak at distance from the microphone, providing superior sound quality with far-field/hands-free speakerphone performance.
System Configurations: Stereo Array Microphone with DSDA beam forming, PureAudio noise reduction and EchoStop full duplex echo cancelation.
ANDREA vs ECHO
Compare the performance of Andrea’s audio filters on the Raspberry Pi 3 vs the Amazon Echo in far-field operation.
ANDREA vs INTEL
Compare the performance of Andrea’s audio filters on the Raspberry Pi 3 vs the Intel Speech Enabling Developer Kit.
- Superior sound quality far-field/hands-free microphone performance.
- Wide frequency response for enhanced accuracy with large vocabulary continuous speech recognition systems.
- Wide bandwidth noise reduction spectrum.
- Stereo microphone digital beam forming for directional sensitivity.
- Full duplex acoustic echo cancelation speakerphone function.
- Low voice distortion and minimal digital artifacts when used in high noise conditions.
- More accurate speech recognition in high noise environments.
- Amplified signal output for interface into any standard microphone input.
- Simple acoustic integration for existing communication system retrofit.
- Robust adaptive algorithms, no tuning required per embodiment.
- Only 10 millisecond latency.
- Low power consumption.
Andrea DA-250Q DSP Microphone Module – Automotive Recordings
DA-250Q OEM Array Microphone-Speakerphone Kit
OEM Subassembly Includes:
- DA-250Q DSP PCBA that can interface with any analog audio system and/or run on any computing device (PC, Mac, Raspberry-Pi, Intel NUC etc.) that has a standard USB audio driver running on the OS (Windows, Mac, Linux etc.).
- 6mm matched microphone pairs in rubber housings with IP67 moisture barriers on a wire-harness.
- Power, signal and echo cancellation cable
- USB-PA audio adapter sound card
- Ground loop power noise isolator
- 3.5mm Y-split cable for AEC reference signal
- 3.5mm male-to-male Aux speaker cable
- 5W audio amplifier (with optional 5V power supply)
- 66mm 8 Ohm midrange loudspeakers that provide ~90dB SPL @1meter
Below are examples of devices that utilize the DA-250Q for voice communications and speech recognition applications:
- Intercom Systems
- Interactive Information Kiosks
- Drive Thru Order Posts
- Embedded Devices
- Natural Language Processing (NLP)