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Grove - Speech Recognizer

SKU C000173 Barcode 101020232 Show more
Original price €0
Original price €25,48 - Original price €25,48
Original price
Current price €25,48
€25,48 - €25,48
Current price €25,48
VAT included

The Grove speech recognizer is a module designed for application in the smart home, toy, robot or anything you would like to control with voice commands.

Overview

The board includes a Nuvoton ISD9160, a microphone, 1 SPI flash, 1 grove connector, 1 speaker connector and 1 led to show to your voice activity.

Nuvoton ISD9160 is (SoC) Chipcorder that based on Cortex™-M0, it provides performance and the energy efficiency needed for voice control applications. The microphone on grove-speech recognizer is Omni-directional.

This speech recognizer can recognize 22 commands including ‘start’, ‘stop’ and ‘Play music’. Every time it recognizes a command, it will return a value and the connected loudspeaker will repeat the command. This value can be used to control other devices like a motor or music player.

Note: The wake up word is “Hicell” (Pronounce it as one word). When it recognizes the awaken word the LED turns red and you can say the command word. If it recognize the command word, the LED will turn blue.

Note: The firmware of the module was wrote by the third party vendor, it’s not open source.

Application Ideas:

  • Internet of Things
  • Smart House
  • Human Machine Interface
  • Lighting Control
  • Sensor Hub
  • Robot

Features:

  • Local Voice Recognition
  • Very low rate of false triggering
  • Speaker connector(JST2.0, speaker is not include)
  • Built-in microphone
  • 3.3/5V working voltage
  • 22 recognition entry
  • Default Baudrate: 9600

Tech specs

Specification

Item

Min

Typ

Max

Condition

Operating Voltage

3V

3.3V

5V

25 ℃

Operating Current

25mA

26.5mA

80mA@playing

VCC = 3.3V 25℃

Operating Current

25mA

26.5mA

130mA@playing

VCC = 5V 25℃

Operating Temperature

0℃

25℃

85℃

Size

40*20mm

Weigth

5g

Flash

2Mbytes

Microphone Sensitivity

-43dB

-40dB

-37dB

VCC = 5V 25℃

Microphone SNR

58dB

Microphone Directivity

Omni-directional

Speaker Power

1W

VCC = 5V 25℃

Processor core

Cortex-M0

Processor Frequency

32.768MHz

50MHz

VCC = 5V 25℃

Get Inspired

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With an array of onboard sensors, Bluetooth® Low Energy connectivity, and the ability to perform edge AI tasks thanks to its nRF52840 SoC, the Arduino Nano 33 BLE Sense is a great choice for a wide variety of embedded applications. Further demonstrating this point, a group of students from the Introduction to Embedded Deep Learning course at Carnegie Mellon University have published the culmination of their studies through 10 excellent projects that each use the Tiny Machine Learning Kit and Edge Impulse ML platform. Wrist-based human activity recognition Traditional human activity tracking has relied on the use of smartwatches and phones to recognize certain exercises based on IMU data. However, few have achieved both continuous and low-power operation, which is why Omkar Savkur, Nicholas Toldalagi, and Kevin Xie explored training an embedded model on combined accelerometer and microphone data to distinguish between handwashing, brushing one’s teeth, and idling. Their project continuously runs inferencing on incoming data and then displays the action on both a screen and via two LEDs. Categorizing trash with sound In some circumstances, such as smart cities or home recycling, knowing what types of materials are being thrown away can provide a valuable datapoint for waste management systems. Students Jacky Wang and Gordonson Yan created their project, called SBTrashCat, to recognize trash types by the sounds they make when being thrown into a bin. Currently, the model can three different kinds, along with background noise and human voices to eliminate false positives. Distributed edge machine learning The abundance of Internet of Things (IoT) devices has meant an explosion of computational power and the amount of data needing to be processed before it can become useful. Because a single low-cost edge device does not possess enough power on its own for some tasks, Jong-Ik Park, Chad Taylor, and Anudeep Bolimera have designed a system where

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