An audio filter that extracts music from noise.
by Chris Lopez
An audio filter that extracts music from noise.
by Chris Lopez
by Karlheinz Essl at rtc-lib
http://www.essl.at/works/rtc.html
Local file: RTC-lib_50_2/put content into patches/RTC-lib/RTC-lib Help/Harmony/infinity-row.maxhelp
At UC Berkeley
http://cse.ssl.berkeley.edu/stereo_solarwind/sounds_programs.html
Max as a Sonification Algorithmic Composition Tool
From algorithmiccomposer.com
Tutorial with example patches in Max and Pd to explore the ideas of making musical sound from weather data. The output is a Midi synthesizer. The patch allows flexible mapping and scaling of data to pitch, tempo, and dynamics.
By Wesley Smith and Graham Wakefield
Tutorial and externals for Max (Jitter) that produce visualization and sonification of scientific data.
By temporarily restoring plasticity to the brain, adults are able learn in ways previously only available to children.
By Rich McCormick at The Verge
“Uncovering Social Relationships through Smartphone Probes”
By: Marco V. Barbera, Alessandro Epasto, Alessandro Mei, Vasile C. Perta, and Julinda Stefa Department of Computer Science, Sapienza University of Rome, Italy,
Using low level wiFi probe-request frames to sense devices and infer characteristics of people carrying those devices. They also use the PNL (preferred network list) to match people based on public SSID’s they have previously accessed.
http://conferences.sigcomm.org/imc/2013/papers/imc148-barberaSP106.pdf
The ever increasing ubiquitousness of WiFi access points, cou- pled with the diffusion of smartphones, suggest that Internet every time and everywhere will soon (if not already has) become a re- ality. Even in presence of 3G connectivity, our devices are built to switch automatically to WiFi networks so to improve user ex- perience. Most of the times, this is achieved by recurrently broad- casting automatic connectivity requests (known as Probe Requests) to known access points (APs), like, e.g., “Home WiFi”, “Campus WiFi”, and so on. In a large gathering of people, the number of these probes can be very high. This scenario rises a natural ques- tion: “Can significant information on the social structure of a large crowd and on its socioeconomic status be inferred by looking at smartphone probes?”.
In this work we give a positive answer to this question. We or- ganized a 3-months long campaign, through which we collected around 11M probes sent by more than 160K different devices. Dur- ing the campaign we targeted national and international events that attracted large crowds as well as other gatherings of people. Then, we present a simple and automatic methodology to build the un- derlying social graph of the smartphone users, starting from their probes. We do so for each of our target events, and find that they all feature social-network properties. In addition, we show that, by looking at the probes in an event, we can learn important sociolog- ical aspects of its participants—language, vendor adoption, and so on.
Retrie Mac address, SSID, and signal strength data of devices within range.
By John Libery at Mlive
http://www.mlive.com/entertainment/kalamazoo/index.ssf/2014/01/project_tracking_mobile_device.html