Posts with «youtube» label

Meet the Video DJ Machine

Have you ever wanted to perform as a DJ but found the equipment expensive as well as intimidating? Well, your prayers have been answered by [Dror Ayalon] who has designed Nomnom 2. It is an open source, music mixing project that uses up to 16 video clips to give you control of your next hit album.

You are given charge of a physical control panel that has 16 buttons and four knobs. Each button can be used to turn on or off a particular clip while the knobs control the repetition rate, volume, speed and playable length of each track. An Arduino sits under the buttons and is responsible for sending the information to an application that runs in your web browser. The browser app uses the NexusUI library to control playback of the audio clips and bring to life the entire experience.

[Dror Ayalon] has been busy polishing his project and there are some neat videos of him demonstrating it so check out the videos below. The code is available for down from GitHub and the BOM is available at the Hackaday.io project page. The web app can be ported to a desktop app using electron and a PCB can be designed for the controller for future versions.

For now, it is incredible to see hardware and software, come together in such a harmonious fashion. This may be the start of something wonderful but if you are just looking for a way to annoy the neighbors, check out the Midi Musical Siren instead.


Filed under: musical hacks
Hack a Day 24 Jul 12:00

oCat is a real-time tracker for popular cat videos

Over the last couple of years, cat videos have become the undisputed champions of the web. Whether it’s kittens playing with their shadows to failed jump attempts to giving each another massages, we’re all guilty of watching a few of these clips from time to time (yes, even at work). Built with this in mind, oCat is a real-time tracker for feline-related activity on the Internet.

oCat consists of two parts: the oCat News Distractor and the Kitty o’Cat Twitter bot. Using Google’s YouTube API, the system works by continuously monitoring for new uploads, the number of new views each day, or a specific video that has received a remarkable amount of attention. It then tweets these stats and prints them out on thermal paper, stamping a paw print on the timeline for every 1,000 views.

Created by Annika Engelhardt, a digital media design master’s student at the University of the Arts in Bremen, oCat uses an Arduino along with an ESP Wi-Fi module, a servo, and an LCD screen. The aim of the project is to increase and reveal the amount of hours people spend watching cat videos online.

The cat is an altered Maneki-neko, holding a stamp using welding wire and hot glue. Even though I filled the stamp with extra ink, it did not work properly and I had to cut out the paw-shape from a sponge and glue it onto the original stamp.

The thermal printer used in the device needs a USB connection, so I used a Raspberry Pi to control it. I wrote a Python script that checks four different RSS news feeds for new posts every 15 minutes and prints one headline with a timestamp every minute.

The Twitter bot was programmed using Python and a library called tweepy. Most of the script is reading JSON files, juggling and comparing data and text files and in the end mixing up parts of a sentence to form a tweet. The bot will be enhanced in the future

Engelhardt exhibited the project at Galerie Flut in Bremen back in October. You can find more pictures and information on the project here.

Arduino Blog 09 Nov 05:49
api  arduino  cat  esp8266  featured  genuino  lcd  servo  wifi  youtube  

Weekend Watch: B2Builds Dives into Electronics and Woodworking

Woodworking and electronics, automatons and camera sliders. Ben Brandt's YouTube channel offers lots of cool projects.

Read more on MAKE

The post Weekend Watch: B2Builds Dives into Electronics and Woodworking appeared first on Make: DIY Projects and Ideas for Makers.

Machine learning for the maker community

At Arduino Day, I talked about a project I and my collaborators have been working on to bring machine learning to the maker community. Machine learning is a technique for teaching software to recognize patterns using data, e.g. for recognizing spam emails or recommending related products. Our ESP (Example-based Sensor Predictions) software recognizes patterns in real-time sensor data, like gestures made with an accelerometer or sounds recorded by a microphone. The machine learning algorithms that power this pattern recognition are specified in Arduino-like code, while the recording and tuning of example sensor data is done in an interactive graphical interface. We’re working on building up a library of code examples for different applications so that Arduino users can easily apply machine learning to a broad range of problems.

The project is a part of my research at the University of California, Berkeley and is being done in collaboration with Ben Zhang, Audrey Leung, and my advisor Björn Hartmann. We’re building on the Gesture Recognition Toolkit (GRT) and openFrameworks. The software is still rough (and Mac only for now) but we’d welcome your feedback. Installations instructions are on our GitHub project page. Please report issues on GitHub.

Our project is part of a broader wave of projects aimed at helping electronics hobbyists make more sophisticated use of sensors in their interactive projects. Also building on the GRT is ml-lib, a machine learning toolkit for Max and Pure Data. Another project in a similar vein is the Wekinator, which is featured in a free online course on machine learning for musicians and artists. Rebecca Fiebrink, the creator of Wekinator, recently participated in a panel on machine learning in the arts and taught a workshop (with Phoenix Perry) at Resonate ’16. For non-real time applications, many people use scikit-learn, a set of Python tools. There’s also a wide range of related research from the academic community, which we survey on our project wiki.

For a high-level overview, check out this visual introduction to machine learning. For a thorough introduction, there are courses on machine learning from coursera and from udacity, among others. If you’re interested in a more arts- and design-focused approach, check out alt-AI, happening in NYC next month.

If you’d like to start experimenting with machine learning and sensors, an excellent place to get started is the built-in accelerometer and gyroscope on the Arduino or Genuino 101. With our ESP system, you can use these sensors to detect gestures and incorporate them into your interactive projects!

ICYMI: Cockroach torture, an app for Parkinson's and more

Today on In Case You Missed It: A cybernetic cockroach how-to describes how to use an Arduino to control where a cockroach goes, which makes all of us uncomfortable.

ICYMI: Cockroach torture, an app for Parkinson's and more

Today on In Case You Missed It: A cybernetic cockroach how-to describes how to use an Arduino to control where a cockroach goes, which makes all of us uncomfortable.

Youtube ‘Loading’ animation with Arduino

How to become YouTube from Thechnocrat on Vimeo.

It may be on a T shirt or it may be on a bag, either way it will look awesome! A simple DIY weekend art using your arduino and a few more common components.

Using an Arduino, a 9-volt battery, and eight ping-pong balls with a white LED inserted into each one, the ring of dots lights up in sequence, endlessly chasing its tail waiting for a non-existant video to load. A giant F5 refresh button would have really completed the costume, though, since that’s everyone’s secret weapon for getting a YouTube clip to finally load.

[Via:Hackaday and Gizmodo]

Arduino Blog 16 Mar 12:56
ar(t)duino  arduino  art  youtube