David Byrne performs at the New Orleans Jazz and Heritage Festival in April.Amy Harris/Invision/AP Since the late-1970s, when David Byrne formed the iconic (and alas, now-defunct) Talking Heads, his career has been an endless stream of fascinating side projects, starting with his super-weird, super-cool Brian Eno collab, My Life in the Bush of Ghosts, and his scoring of choreographer Twyla Tharp's The Catherine Wheel. He founded his own World Music label, Luaka Bop, and wrote half a dozen books, including the best-selling quasi-memoir How Music Works. His obsession with the National Color Guard Championships led to a documentary called Contemporary Color. Most recently, his American Utopia tour featured dancers and musicians untethered from the standard concert setup by means of wireless and wearable instruments--nary an amp nor drumset in sight. In November, as the tour wrapped up, came the re-release of Byrne's 1986 film, True Stories, which explores the inner lives and outer quirks of residents of a fictional Texas town and is based on stories from tabloid newspapers.

Myra and Gia were godsend. If tourists start booking Shimla holidays to enjoy the snow and traffic outstrips the number of rooms, the duo sends alerts to hotel chains in the region to increase online inventory. They forecast demand, answer customer queries and can even update on flight delays. The duo has been at the travel portal's headquarters in Cyber City, Gurgaon, for just a year but has already moved on to solving more difficult tasks such as managing loyalty programmes. Whether cancellations or refunds from 12,000-15,000 customer interactions every day, about 60% are resolved by the duo and some of their other techsavvy mates.

The Game Awards used to be considered the video game industry's Oscars. Now, the event, in its fifth year and set to be streamed live around the globe Thursday night, is becoming more like the Emmys. That's because games are changing, says the show's creator and executive producer, Geoff Keighley. Just as the Emmys awards seasons of TV shows, "we almost have seasons of games now. With'Fortnite,' even though it came out last year, it's still nominated this year because they keep updating it," Keighley said.

This paper considers extractive summarisation in a comparative setting: given two or more document groups (e.g., separated by publication time), the goal is to select a small number of documents that are representative of each group, and also maximally distinguishable from other groups. We formulate a set of new objective functions for this problem that connect recent literature on document summarisation, interpretable machine learning, and data subset selection. In particular, by casting the problem as a binary classification amongst different groups, we derive objectives based on the notion of maximum mean discrepancy, as well as a simple yet effective gradient-based optimisation strategy. Our new formulation allows scalable evaluations of comparative summarisation as a classification task, both automatically and via crowd-sourcing. To this end, we evaluate comparative summarisation methods on a newly curated collection of controversial news topics over 13 months. We observe that gradient-based optimisation outperforms discrete and baseline approaches in 15 out of 24 different automatic evaluation settings. In crowd-sourced evaluations, summaries from gradient optimisation elicit 7% more accurate classification from human workers than discrete optimisation. Our result contrasts with recent literature on submodular data subset selection that favours discrete optimisation. We posit that our formulation of comparative summarisation will prove useful in a diverse range of use cases such as comparing content sources, authors, related topics, or distinct view points.

The telecoms operator's much-awaited new set-top box, dubbed Freebox Delta and designed by Jasper Morrison, will also contained an improved internet speed and data storage, as well as the audio technology of French start-up firm Devialet.

An article by Apple describing how the use machine learning to improve HomePod's signal processing system to remove echo, background noise and separate sound sources.

Already, AI is more widespread than many of us may realise. Spotify, Netflix and Amazon all use it to assist in the way they target their products to consumers--while Amazon has also used it to reduce labour costs, effectively replacing six humans with one robot. In this article, we take a look at how investors should think about AI, how widespread it is, and why this thematic is likely to have a far-reaching impact on economies and markets. We also examine why those companies that are embracing AI are likely to be able to both disrupt and deliver real value to their shareholders. At a company level, the ability to implement and leverage AI will become one of the key differentiators over the next decade.

The dawn of 8K resolution television has arrived as a Japanese television network broadcasts the first programmes in the format. Japan's national public broadcasting organisation, NHK, is now sending both 4K and 8K channels via satellite to viewers. The first film shown in 8K will be '2001: A Space Odyssey' and Stanley Kubrick's classic film was scanned by Warned Bros on 70mm film negatives to produce the high-resolution masterpiece. This newest resolution has four times as many pixels vertically and horizontally as current top-end 4K Ultra HD screens, and 16 times more than standard 1080p HD. NHK is currently the only channel pushing for the 8K medium as specialist equipment and huge price-tags puts off consumers and companies alike.

In this paper, we propose a movie genre recommendation system based on imbalanced survey data and unequal classification costs for small and medium-sized enterprises (SMEs) who need a data-based and analytical approach to stock favored movies and target marketing to young people. The dataset maintains a detailed personal profile as predictors including demographic, behavioral and preferences information for each user as well as imbalanced genre preferences. These predictors do not include movies' information such as actors or directors. The paper applies Gentle boost, Adaboost and Bagged tree ensembles as well as SVM machine learning algorithms to learn classification from one thousand observations and predict movie genre preferences with adjusted classification costs. The proposed recommendation system also selects important predictors to avoid overfitting and to shorten training time. This paper compares the test error among the above-mentioned algorithms that are used to recommend different movie genres. The prediction power is also indicated in a comparison of precision and recall with other state-of-the-art recommendation systems. The proposed movie genre recommendation system solves problems such as small dataset, imbalanced response, and unequal classification costs.

Separating a singing voice from its music accompaniment remains an important challenge in the field of music information retrieval. We present a unique neural network approach inspired by a technique that has revolutionized the field of vision: pixel-wise image classification, which we combine with cross entropy loss and pretraining of the CNN as an autoencoder on singing voice spectrograms. The pixel-wise classification technique directly estimates the sound source label for each time-frequency (T-F) bin in our spectrogram image, thus eliminating common pre-and postprocessing tasks. The proposed network is trained by using the Ideal Binary Mask (IBM) as the target output label. The IBM identifies the dominant sound source in each T-F bin of the magnitude spectrogram of a mixture signal, by considering each T-F bin as a pixel with a multi-label (for each sound source). Cross entropy is used as the training objective, so as to minimize the average probability error between the target and predicted label for each pixel. By treating the singing voice separation problem as a pixel-wise classification task, we additionally eliminate one of the commonly used, yet not easy to comprehend, postprocessing steps: the Wiener filter postprocessing. The proposed CNN outperforms the first runner up in the Music Information Retrieval Evaluation eXchange (MIREX) 2016 and the winner of MIREX 2014 with a gain of 2.2702 5.9563 dB global normalized source to distortion ratio (GNSDR) when applied to the iKala dataset. This work is supported by the MOE Academic fund AFD 05/15 SL and SUTD SRG ISTD 2017 129. Corresponding Author D. Herremans Singapore University of Technology and Design, Singapore & Institute for High Performance Computing, A*STAR, Singapore Email: dorien herremans@sutd.edu.sg 1 INTRODUCTION to compete with cutting-edge singing voice separation systems which use multichannel modeling,data augmentation, and model blending. Keywords Singing Voice Separation · Convolutional Neural Network · Ideal Binary Mask · Cross Entropy · Pixel-wise Image Classification 1 Introduction Humans have an exceptional ability to separate different sounds from a musical signal [3]. For instance, some musicians can distinguish the guitar part from a song and transcribe it; and most non-musician listeners are able to hear and sing along to lyrics of a song.