The graph above plots tempi in the first movement, in terms of average beats per minute; the fastest is Hermann Scherchen, at 174.58, and the slowest is Otto Klemperer, in 1970, at 110.74.
Read more: http://www.newyorker.com/online/blogs/alexross
(Thanks for the tip, Adam)
From AT&T’s lab. A nifty geographic representation of musical artist. Zoom in and out to find artists.
Creator: AT&T
Uses: GMAP – a technique for visualizing relations and structures as maps
Hot of the presses, here are the sides for the tutorial that Justin and Paul are presenting at ISMIR 2009 on October 26.
Note that the live presentation will include many demonstrations and videos of visualizations that just are not practical to include in a PDF. If you have the chance, be sure to check out the tutorial at ISMIR in Kobe on the 26th.
Last.fm has added a few visualizations to their VIP (subscribers only) section of their playground. One visualization is called Tube Tags – it will represent your listening history in the style of the London Tube. Each colored line represents a genre / social tag:
It’s an attractive visualization drawing on the design of the original tube map designer Harry Beck
This project explores several different ways of visualizing sets of extracted audio features in real-time. These visualizations are realized in a toolkit for the Max/MSP/Jitter programming environment. The primary purpose is to visualize timbral changes in the sense of exploratory data analysis. The program has four main parts: feature extraction, visualization, similarity, and audio control. Features are calculated by using a combination of pre-existing libraries, as e.g. the zsa.descriptors and the CNMAT analyser object. Additionally, we introduce a simple notion of timbral distance, which can be used in real-time performance situations, and present its performance for a set of different textures. The visualizations are further used to inform the control of audio effects by feature trajectories.
Researcher: Kai Siedenburg
Paper: An Exploration of Real-Time Visualizations of Musical Timbre
As part of our tutorial on music visualizations at ISMIR at the end of the month we’ll be surveying the wide range of personal, human-rendered visualizations of the music space. There’s a wide variety of such visualizations – but one of my favorites, is the Velvet Underground Evolutionary Tree
put together by the author of the Kenosha Kid’s blog. It’s a phylogenetic tree with the Velvet Underground as the common ancestor – it has bands as the branches, and music genres as the leaves (along with a stray calamari), and a dig at the ‘barnacle’ power pop critic Steve Simels. This visualization has it all!
The focus of our upcoming tutorial at ISMIR is on all the different ways that individuals have tried to represent music collections visually. After a lengthy review of all the personal, popular, and academic music visualizations, we find the purpose of this process can range from artistic self expression to a more straight forward, utilitarian role. One noticeably absent (or at least underrepresented) form of visualization is for analysis of music. Rather than producing highly polished representations of music corpuses for public display, we want to talk about visualizations that can be used to help understand how computers are relating music relationships algorithmically, perhaps for the purposes of genre classification. In general, we’re interested if we can better understand the behavior and performance of music information retrieval systems through visualization, enhancing the simple numeric measurements such as precision and recall.
Justin has produced a short video where he talks about a method for visualizing acoustic and tag based metadata using the open source statistical language R.
We are looking forward to Japan, and hope that you join us on the 26th!
Paul and Justin
Joris Klerkx has built a visualizer of the emotions in lyrics. Joris has integrated a karaoke player and Synesketch, a framework for visualizing 6 basic emotions, defined by Ekman (happiness, anger, fear, surprise, sadness, disgust). The player takes a song, plays it, and with each line of text that plays in the lyrics, the strongest emotion of that line is visualized. In the image above, on the left hand side, you’ll see the 6 emotions and their visualization. On the right hand side, 2 screenshots of demo’s of the prototype.
Some video of the player in action:
Jorik points out that it can be interesting to see how the visualizations contrast with how the song sounds since offten times the emotion and mood of the lyrics of a song contrast with how the song sounds
Creator:
Zune offers a rather rich music browsing experience on the web showing all sorts of artist info including songs, videos, bios, news, reviews and artist popularity data. One rather nifty tool is their MixView. When browsing an artist you can click on MixView to display a variety of information related to the seed artist in various-sized boxes. Each box is clickable, which brings focus of the related item into view, and in turn, a new set of related boxes appear. Additionally, each box has other actions such as “play” and “learn more” depending on the view that allows the user to jump to different places in the Zune Marketplace. I like how MixView combines different types of information in one view. In one view they show related artist, artist influences, artist albums, related albums and so on. It is a well done browser – and one of the first that I’ve seen implemented in Silverlight.
This quick video shows off MixView.
Creator:
Submitted by Tom Butcher
MarGrid is a visualization that uses Self-Organizing Maps to organize music collections into a two-dimensional grid based on music similarity. On the MarGrid website you can use find a flash-based interface that will let you explore a 1,000 song music collection.
The MarGrid interface is incorporated into AudioScapes, a framework for prototyping and exploring how touch-based and gestural controllers can be used with state-of-the-art content and context-aware visualizations. AudioScapes provides well-defined interfaces and conventions a variety of different audio collections, controllers and visualization methods so they can be easily combined to create innovative ways of interacting with large audio collections.
Here’s an AudioScape video that shows the MarGrid in an iPhone app that is designed to to help people with disabilities navigate through their personal collections. There are more videos worth watching on the AudioScapes site.
Creator:
MarGrid and AudioScapes is a project being built by researcher Steven Ness and George Tzanetakis at the University of Victoria It is built using the venerable Marsyas audio framework
More Info:
