The NIME conference traditionally focuses on interfaces for music and musical expression. In this paper we reverse this tradition to ask, can interfaces developed for music be successfully appropriated to non-musical applications? To help answer this question we designed and developed a new device, which uses interface metaphors borrowed from analogue synthesisers and audio mixing to physically control the intangible aspects of a Large Language Model. We compared two versions of the device, with and without the audio-inspired augmentations, with a group of artists who used each version over a one week period. Our results show that the use of audio-like controls afforded more immediate, direct and embodied control over the LLM, allowing users to creatively experiment and play with the device over its non-mixer counterpart. Our project demonstrates how cross-sensory metaphors can support creative thinking and embodied practice when designing new technological interfaces.

In unsupervised anomaly detection (UAD) research, while state-of-the-art models have reached a saturation point with extensive studies on public benchmark datasets, they adopt large-scale tailor-made neural networks (NN) for detection performance or pursued unified models for various tasks. Towards edge computing, it is necessary to develop a computationally efficient and scalable solution that avoids large-scale complex NNs. Motivated by this, we aim to optimize the UAD performance with minimal changes to NN settings. Thus, we revisit the reconstruction-by-inpainting approach and rethink to improve it by analyzing strengths and weaknesses. The strength of the SOTA methods is a single deterministic masking approach that addresses the challenges of random multiple masking that is inference latency and output inconsistency. Nevertheless, the issue of failure to provide a mask to completely cover anomalous regions is a remaining weakness. To mitigate this issue, we propose Feature Attenuation of Defective Representation (FADeR) that only employs two MLP layers which attenuates feature information of anomaly reconstruction during decoding. By leveraging FADeR, features of unseen anomaly patterns are reconstructed into seen normal patterns, reducing false alarms. Experimental results demonstrate that FADeR achieves enhanced performance compared to similar-scale NNs. Furthermore, our approach exhibits scalability in performance enhancement when integrated with other single deterministic masking methods in a plug-and-play manner.

When it comes to advanced features and seamless compatibility with iOS devices, Apple Music has Spotify well and truly beaten. The Swedish streaming giant has essentially the same content library as Apple and better music discovery algorithms, but Apple Music has the technical edge with support for lossless audio, spatial sound that works in Apple's super popular earbuds and over-ear headphones, and one of the best features on the market for displaying lyrics. And now, Apple is revealing yet another ace up its technological sleeve. Apple Music Sing, available later this month, will give subscribers the ability to transform millions of the platform's most popular songs into lyric-free sing-alongs, all powered by machine intelligence and proprietary processing technology. On supported songs in Apple Music, a fader will appear that lets the user turn down the vocals and sing along to the tune.

When it comes to motion tracking and music, you can follow the breadcrumbs including Max Mathews, Imogen Heap, Beat Saber, a growing research and Kickstarter crowd, and now potentially you. If you're a DJ using djay Pro AI on an iPad Pro running iOS 14 (utilizing Apple's Vision Framework) today's the day you join the party. An update to Algoriddim's djay software is now available, and while it includes various new tweaks, the most notable is a touchless Gesture Control interface. You'll still need your hands for track selection, volume and occasionally the fader, but you can trigger automatic transitions, filter sweeps, scratching and loops with the motion of your hands… placed carefully over an iPad Pro in a well-lit area. So, essentially an environment unlike most DJ booths, but you have to start somewhere.

A growing number of organizations are turning to algorithms to "score" their customers. Understanding customers and rewarding the best has always been at the center of running a successful business. However, thanks to big data and analytics, the concept is changing. A growing number of organizations are turning to algorithms to "score" customers and determine what price they pay, how long they must wait on hold, and whether they can return items they have purchased. "Digital technology has enabled consumers to be more knowledgeable about prices, and online shopping has made it easier for them to hunt for bargains," explains Sunil Gupta, Edward W. Carter Professor of Business Administration and chair of the General Management Program at Harvard University.

Ten years ago, the most forward-thinking companies were just starting to dive into the potential of data and analytics. Since then, brands have moved from using analytics to answer what customers are doing to exploring the how and why, and also to figure out what they will do in the future. The Wharton Customer Analytics Initiative (WCAI) is celebrating its 10th anniversary this year and has seen every step of that evolution. Knowledge@Wharton recently sat down with Wharton marketing professors Eric Bradlow, Peter Fader and Raghuram Iyengar to discuss how the field has developed over time, and what they expect to be the key trends over the next decade. Bradlow and Fader are the founding directors of WCAI, and Bradlow and Iyengar are the current co-directors. An edited transcript of the conversation follows. Knowledge@Wharton: When WCAI launched ten years ago, what was the environment like in the field of analytics? What were some of the initiative's key goals? Peter Fader: Even though the initiative started 10 years ago, the idea started about a decade before that.

How Elton John's VR retirement announcement hit your headset Emma Grey Ellis, Wired If you haven't heard, music legend Sir Elton John announced his retirement and final tour this week in the most 2018 way possible: VR. Wired goes behind the scenes to get the details on how that montage was made before it beamed out to your headset. MLB's Advanced Media arm does a lot of things -- from websites to streaming. It's also working on a video game and Polygon has a detailed look at the development. Spotify's scientist: Artificial intelligence should be embraced, not feared, by the music business Tim Ingham, Music Business Worldwide MBW caught up with the director of Spotify's Creator Technology Research Lab to chat about AI and how it relates to the future of the biz, including robot musicians.