Many ideas were based on a paper by Yan Ke, Derek Hoiem, and Rahul Sukthankar called “Computer Vision for Music Identification” (2005). In fact, even the Last.fm fingerprinter uses the code published by the authors of this paper. This is where I learned that audio identification is more about machine learning that it is about DSP. Many useful methods for extracting interesting features from audio streams are well-known and the problem is more about how to apply and index them the best way. The basic idea here is to treat audio as a spectral image and index the content of the image. I’ll explain this in more detail and how Chromaprint uses this in a following post. Another important paper for me was “Pairwise Boosted Audio Fingerprint” (2009) by Dalwon Jang, Chang D. Yoo, Sunil Lee, Sungwoong Kim and Ton Kalker (Ton Kalker is a co-author of a historically important paper “Robust Audio Hashing for Content Identification” (2001) published by Philips Research), which combined previous experiments of the authors with audio identification based on spectral centroid features and the indexing approach similar to the one suggested by Y. Ke, D. Hoiem and R. Sukthankar. For a long time this was the best solution I had and since it was actually not very hard to implement, the most time I spent on tweaking the configuration to get the best results. The last major change came after I learned about “chroma” features by reading the “Efficient Index-Based Audio Matching” (2008) by Frank Kurth and Meinard Müller. I’ve read more papers about chroma features later, but this was the first and also the most important one for me and some ideas about processing the feature vectors from it are implemented in Chromaprint. Chroma features are typically used for music identification, as opposed to audio file identification, but I tried to use them with the approach I already had implemented and it nicely improved the quality of the fingerprinting function and actually reduced complexity which allowed me to use much larger training data sets.
Instead, the stories could become worlds inhabited by things that keep slipping beyond our grasp. Things which lurk at the back of our mind, on the tip of our tongue, just out of reach. Stories with protagonists that can only be known as gaps in being. The spaces they leave. Not here and not quite there yet. Dwelling on the peripheries of the sensible, speaking in glimmers, shimmers, suggestions.
These stories may not even have words. They might be felt rather than told. In sound, scent, touch and light. The stories might be experienced at the limits of the visible spectrum, pulsing at ultraviolet or infrared frequencies. They might inhabit the radio spectrum or create divergencies across the spectrum of acceptable behaviours. Spectral stories, stories of cosmic spectra and planetary spectres. The folk tales of unquiet matter.
“I have sought to show how the terms ‘mind’ and 'matter’ are abstractions which in their concreteness are identical” —Peter Sjöstedt-H
Recorded and composed in the Sonoran Desert, Seili, the Kii peninsula, Istria, Helsinki, Brussels and Elsewhere during 02018 and 02019 by Maja Kuzmanovic and Nik Gaffney
“Spectrality, the way a thing keeps exceeding itself, or is displaced from itself, or is ecstatically outside itself (ekstasis, “ex-sistence”), doesn’t just belong to human being […] Humankind is flickering, displaced from itself, ecstatic, rippling and dappled with shadows. Shadows made not only by some other entity interacting with it, like the sun through the trees, but shadows that are an intrinsic part of the thing.”
Today’s ALPHA result is the first observation of a spectral line in an antihydrogen atom, allowing the light spectrum of matter and antimatter to be compared for the first time. Within experimental limits, the result shows no difference compared to the equivalent spectral line in hydrogen. This is consistent with the Standard Model of particle physics, the theory that best describes particles and the forces at work between them, which predicts that hydrogen and antihydrogen should have identical spectroscopic characteristics. ALPHA is a unique experiment at CERN’s Antiproton Decelerator facility, able to produce antihydrogen atoms and hold them in a specially-designed magnetic trap, manipulating antiatoms a few at a time. Trapping antihydrogen atoms allows them to be studied using lasers or other radiation sources.