Localizing Fireworks Launches With A Raspberry Pi

If you have multiple microphones in known locations, and can determine the time a sound arrives at each one, you can actually determine the location that sound is coming from. This technique is referred to as sound localization via time difference of arrival. [Kim Hendrikse] decided to put the technique to good use to track down the location of illicit fireworks launches.

The build is based on the Raspberry Pi, with [Kim] developing an “autonomous recording unit” complete with GPS module for determining their location and keeping everything time synchronized. By deploying a number of these units, spread out over some distance, it’s possible to localize loud sounds based on the time stamps they show up in the recording on each unit.

Early testing took place with an air horn and four recording units. [Kim] found that the technique works best for sounds made within the polygon.  Determining the location was achieved with a sound investigation tool called Raven Lite, developed by Cornell University. The process is very manual, involving hunting for peaks in sound files, but we’d love to see a version that automated comparing sound peaks across many disparate recording units. In any case, it worked incredibly well for [Kim] in practice. Later testing with friends and a network of six recorders spread over Limburg, Netherlands, [Kim] was later able to localize fireworks launches with an accuracy down to a few meters.

Similar techniques are used to locate gunshots, and can work well with pretty much any loud noise that’s heard over a great distance. If you’ve been using your hacker skills to do similar investigative work, don’t hesitate to let us know on the tipsline!

Seeed Studio’s ReSpeaker Speaks All The Voice Recognition Languages

Seeed Studio recently launched its third Kickstarter campaign: ReSpeaker, an open hardware voice interface. After their previous Kickstarted IoT hardware, such as the RePhone, mostly focused on connectivity, the electronics manufacturer from Shenzhen now tackles another highly contested area of IoT: Voice recognition.

The ReSpeaker Core is a capable development board based on Mediatek’s MT7688 WiFi module and runs OpenWrt. Onboard is a WM8960 stereo audio codec with integrated 1W speaker/headphone driver, a microphone, an ATMega32U4 coprocessor, 12 addressable RGB LEDs and 8 touch sensors. There are also two expansion headers with GPIOs, I2S, I2C, analog audio and USB 2.0 and an onboard microSD card slot.

The latter is especially useful to feed the ReSpeaker’s integrated speech recognition engine PocketSphinx with a vocabulary and audio file library, enabling it to respond to keywords and commands even when it’s not hooked up to the internet. Once it’s online, ReSpeaker also supports most of the available cloud based cognitive speech recognition services, such as Microsoft Cognitive Service, Amazon Alexa Voice Service, Google Speech API, Wit.ai and Houndify. It also comes with an SDK and Python API, supports JavaScript, Lua and C/C++, and it looks like the coprocessor features an Arduino-compatible bootloader.

The expansion header accepts shield-like hardware add-ons. Some of them are also available through the campaign. The most important one is the circular, far-field microphone array. Based on 7 XVSM-2000 respeaker_meow2digital microphones, the extension board enhances the device’s hearing with sound localization, beam forming, reverb and noise suppression. A Grove extension board connects the ReSpeaker to the Seeed’s current lineup on ready-to-use sensors, actuators and other peripherals.

Seeed also cooperates with the Meow King Audio Electronic Company to develop a nice tower-shaped enclosure with built-in speaker, 5W amplifier and battery. As a portable speaker, the Meow King Drive Unit (shown on the right) certainly doesn’t knock your socks off, but it practically turns the ReSpeaker into an open source version of the Amazon Echo — including the ability to run offline instead of piping everything you say to Big Brother.

According to Seeed, the freshly baked hardware will ship to backers in November 2016, and they do have a track-record of on-schedule shipped Kickstarter rewards. At the time of writing, some of the Crazy Early Birds are still available for $39. Enjoy the campaign video below and let us know what you think of think hardware in the comments!

Sound Localization And A Treaded Rover


[Jad] recently wrote in to share a pair of projects that have been keeping him busy as of late.

The first is a sound localization system not unlike one we showed you a few weeks ago. The difference is that his system displays the sound source via a set of LEDs rather than by motion, making it far less prone to interference by things like servo noise. His system uses four identical circuits, each of which are wired to a separate analog input on the Arduino. Each channel is adjustable, making it easy to tweak how the system responds to a particular sound.

His second project is a sizable robot built on the Motoruino platform. His contraption features several stacked control boards that handle the bots locomotion as well as camera control. It connects to his computer via a Bluetooth module that boasts a 1 mile range, allowing him to control everything from his PC. [Jad] is using the robot as a prototype for a much larger scale creation, and he says that his current focus is getting the robot to track and follow objects automatically using the on-board camera.

Continue reading to see a small preview of his bot’s progress so far.

Continue reading “Sound Localization And A Treaded Rover”

DIY Sound Localization Sensor


Sound localization is very popular in law enforcement circles due to its accuracy and ability to quickly separate gunshots from other similar noises. These systems don’t come cheap, and after trying to build one himself, [Fileark] knows why.

He thought it would be neat to build a sound localization sensor based on how the human ear determines a sound’s source. Once he got started however, he realized just how hard it was to do localization just right.

He used an LM324N op-amp as a volume comparator, which he says works decently enough though he figures there are ICs out there that can do a better job. [Fileark] reports that the sound detector works well when the source is within about a foot of the sensors, but performance deteriorates at greater distances. He may consider using an ARM Cortex-M3 as his sound processor if he builds a second version, since the Arudino he used just doesn’t have enough power to sample and run calculations within the 10-50 microsecond window he requires.

Keep reading to see a video of his sound localization sensor in action.

Continue reading “DIY Sound Localization Sensor”