The JM Jazz World Orchestra is the first global youth jazz orchestra project of Jeunesses Musicales International. The orchestra brought together some of the world’s top young professional jazz musicians from over 9 different nationalities to form an amazing big band with an internationally diverse repertoire under the direction of renowned Sigi Feigl.
Check out this report by Branko Banjac who followed them everywhere, from the Summer Jazz Academy Camp in Groznjan (Croatia) to their performances in Izola (Slovenia) and Weikersheim (Germany).
Eight groups, including Google and Amazon, have submitted applications with the Internet Corporation for Assigned Names and Numbers (ICANN) to be the exclusive operator of the .music domain.
The identities of the groups vying for the .music and other generic top-level domains (gLTD) were part of ICANN's unveiling Wednesday of 1,900 applications for new gLTDs (which cost $185,000 to submit). A gLTD is an Internet domain name extension, such as .com or .org. ICANN has developed its generic top-level domain program to increase competition and introduce new gLTD's into the Internet's addressing system. Only 22 gLTDs are currently in the domain name system right now and another 280 are reserved for countries.
There was something for everyone. Music was all pervasive. We weren't just buying music from our favourite bands but anything that got recommended by the thriving music press or something that got fished out of a bargain bucket. My favourite bands were called Kyuss and The God Machine there were loads of great minor bands to pick from. (addendum: the intro/outro of The God Machine's Home even got me looking into World Music).
But then it all started going wrong.
The beginning of the end
Mid 1994 might sound like an early start of the decline - there were some great releases that year - but pointedly, most came from existing bands. Guns N Roses were becoming a caricature of themselves. Metallica weren't doing much new and then, out of the blue, Kurt Cobain shot himself. This was incredibly upsetting to me and millions of others and it rather emphasised a change that was in the air. Music (new music in particular) was losing its edge and becoming more commercial.
The number I’ve heard recently is that there are about 200 million music buyers in the world. And there are about seven billion people in the world. So, if we can make that 200 million grow to 250 million, we can make a little bit more money. But that would only take the net world music business from $16 billion to $20 billion. It won’t take it back to its peak in 1999. It will just make it a little bigger.
The audience as co-creators—a challenge for composers of interactive musicPhys.OrgAnother challenge is to design a computer programme and physical interface that motivate co-creators in different situations to interact and create music.
Sleep musicalization is a novel way of perceiving and experiencing sleep measurement data. The goal is to help users understand and analyze their sleeping patterns and eventually improve their sleep.
The musicalization process follows musicological principles when composing a melody, designing the rhythm and changes in tempo, arranging the accompaniment, and playing out the music at different levels of volume. These aspects are inspired but not dictated by the data. The result of musicalization of eight hours of sleep is an origianal piece of couple of minutes of music.
Musicalization of data provides a whole new way to experience data as a music. Music has a unique capability to invoke emotions, giving users a novel opportunity to perceive their data also as innate feelings. In the case of sleep measurements, musicalization complements the more informative, no-nonsense visual results with an emotional component towards one's own sleep.
Sleep analysis in this service is based on the Beddit sleep measurement sensor. Sleep measurements and analysis results are retrieved from the Beddit service, by the user's authorization, and then musicalized by composing a novel piece of music.
A documentary about Share Conference held in Belgrade, in April 2011. It was the first of its kind and it gathered the leaders in digital activism, artists from the field of new media and recognized musicians.
Don Passman is an entertainment lawyer and author of the essential book, "All You Need to Know About the Music Business." In this clip, Don Passman talks to http://www.artistshousemusic.org about the current state of the music business and where he thinks the business is headed.
System classifies music using a mix of human and artificial intelligence...
If you wanted to find every jazz tune on the Internet that paired a harmonica with a saxophone, you could enter keywords for the instruments, but your results would be meager. That’s because verbal tagging for music hardly exists. Now comes a new method that promises to use machines, trained by human beings, to classify the vast trove of music and video that’s in cyberspace. It’s an approach midway between the two systems that have so far been applied in recommender systems. The first method, called collaborative filtering, uses an algorithm to infer your taste in music, movies, or books from your past choices, then suggests new materials enjoyed by other people with similar taste. However, collaborative filtering’s no good at categorizing things that aren’t already popular. The second method uses human experts to classify things, as Pandora.com has done with its Music Genome project. The experts actually listen to each song, then fit it into a number of categories so that customers who name a song they like can be presented with a selection of similar music. But though Pandora’s musicians have thus categorized nearly a million songs in the past dozen years, there’s no way they can handle the 60 hours of multimedia that’s uploaded to YouTube every minute. The new approach is something of a hybrid of man and machine. The researchers got tagging information from human participants in the music annotation game “Herd It” and fed it into a machine-learning program, which then sought out additional information from the human informants. The program thus refines the model through several iterations until it can reliably mimic the humans. At that point the machine can go to work classifying music. The researchers say their method can be scaled up easily, which means that it should be able to search the Internet’s enormous inventory of music and video. Such man-machine collaborations seem to be taking over many facets of artificial intelligence. Call it artificially enhanced intelligence—or naturally enhanced AI. In so-called Advanced Chess, for instance, human/machine combos known as “centaurs” regularly outplay the best human chess masters—and the best chess machines. “This pattern is true not only in chess, but throughout the economy,” wrote MIT economists Erik Brynjolfsson and Andrew McAfee in the Sloan Management Review, in December “In medicine, law, finance, retailing, manufacturing and even scientific discovery, the key to winning the race is not to race against machines, but to win using machines.” Of course, nobody’s talking about using machines—alone or with coaching—to judge the artistic value of a piece of music. Yet.
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