One aim of data mining is the identification of interesting structures in data. Basic properties of the empirical distribution, such as skewness and an eventual clipping, i.e., hard limits in value ranges, need to be assessed. Of particular interest is the question, whether the data originates from one process, or contains subsets related to different states of the data producing process. Data visualization tools should deliver a sensitive picture of the univariate probability density distribution (PDF) for each feature. Visualization tools for PDFs are typically kernel density estimates and range from the classical histogram to modern tools like bean or violin plots. Conventional methods have difficulties in visualizing the pdf in case of uniform, multimodal, skewed and clipped data if density estimation parameters remain in a default setting. As a consequence, a new visualization tool called Mirrored Density plot (MD plot) is proposed which is particularly designed to discover interesting structures in continuous features. The MD plot does not require any adjustments of parameters of density estimation which makes the usage compelling for non-experts. The visualization tools are evaluated in comparison to statistical tests for the typical challenges of explorative distribution analysis. The results are presented on bimodal Gaussian and skewed distributions as well as several features with published pdfs. In exploratory data analysis of 12 features describing the quarterly financial statements, when statistical testing becomes a demanding task, only the MD plots can identify the structure of their pdfs. Overall, the MD plot can outperform the methods mentioned above.

--Melody generation from lyrics has been a challenging research issue in the field of artificial intelligence and music, which enables to learn and discover latent relationship between interesting lyrics and accompanying melody. Unfortunately, the limited availability of paired lyrics-melody dataset with alignment information has hindered the research progress. T o address this problem, we create a large dataset consisting of 12,197 MIDI songs each with paired lyrics and melody alignment through leveraging different music sources where alignment relationship between syllables and music attributes is extracted. Most importantly, we propose a novel deep generative model, conditional Long Short-T erm Memory - Generative Adversarial Network (LSTM-GAN) for melody generation from lyrics, which contains a deep LSTM generator and a deep LSTM discriminator both conditioned on lyrics. In particular, lyrics-conditioned melody and alignment relationship between syllables of given lyrics and notes of predicted melody are generated simultaneously. Experimental results have proved the effectiveness of our proposed lyrics-to-melody generative model, where plausible and tuneful sequences can be inferred from lyrics. I NTRODUCTION Music generation is also referred to as music composition with the process of creating or writing an original piece of music, which is one of human creative activities [1]. Without understanding music rules and concepts well, creating pleasing sounds is impossible. To learn these kinds of rules and concepts such as mathematical relationships between notes, timing, and melody, the earliest study of various music computational techniques related to Artificial Intelligence (AI) has emerged for music composition in the middle of 1950s [2]. Markov models as a representative method of machine learning have been applied to algorithmic composition [3]. However, due to the limited availability of paired lyrics-melody dataset with alignment information, research progress of lyrics-conditioned music generation has been obstructed.

The two companies have butted heads for years, and it's likely they'll continue to do so--Spotify's protest web page (in which Spotify details accusations that Apple engages in anticompetitive behavior) is just one example of hurt feelings. But despite the mutual dislike, Apple and Spotify are reportedly in talks to integrate Spotify more tightly with Siri, Apple's digital assistant. The companies are "discussing a plan" that would let iPhone users ask Siri to play music with Spotify, instead of requiring them to manually navigate to whatever song, album, or playlist they want to hear via the third-party app. The Information's report on this handy potential change cites three anonymous sources who are "familiar with the discussions." Neither company confirmed the report when contacted by Fast Company.

In this paper, we generalize the basic notions and results of Dempster-Shafer theory from predicates to formal concepts. Results include the representation of conceptual belief functions as inner measures of suitable probability functions, and a Dempster-Shafer rule of combination on belief functions on formal concepts.

For over 50 years, researchers have been trying to teach computers to read music notation, referred to as Optical Music Recognition (OMR). However, this field is still difficult to access for new researchers, especially those without a significant musical background: few introductory materials are available, and furthermore the field has struggled with defining itself and building a shared terminology. In this tutorial, we address these shortcomings by (1) providing a robust definition of OMR and its relationship to related fields, (2) analyzing how OMR inverts the music encoding process to recover the musical notation and the musical semantics from documents, (3) proposing a taxonomy of OMR, with most notably a novel taxonomy of applications. Additionally, we discuss how deep learning affects modern OMR research, as opposed to the traditional pipeline. Based on this work, the reader should be able to attain a basic understanding of OMR: its objectives, its inherent structure, its relationship to other fields, the state of the art, and the research opportunities it affords.

LOS ANGELES, Aug. 13 (Xinhua) -- Researchers at University of Utah Health are using machine learning to draw links between genetic controls that …

The big subset of AI that everyone's working in now is machine learning . That's where machines can actually learn from massive amounts of data, …

Artificial intelligence is finding its way into every industry and insurance is no exception.

Chinese search engine Sogou is creating artificial-intelligence lookalikes to read popular novels in authors' voices. It announced "lifelike" avatars of Chinese authors Yue Guan and Bu Xin Tian Shang Diao Xian Bing - created from video recordings - at the China Online Literature conference. Last year, Sogou launched two AI newsreaders, which are still used by the government's Xinhua news agency. Appetite for audiobooks in China is on the rise, mirroring trends in the West. Chinese think tank iiMedia expects the market to more than double between 2016 and 2020, to 7.8bn Chinese yuan (£900m) a year. It is now a simple process to use text-to-speech technology to quickly generate an audio version of a book, using digitised, synthetic voices.

We were on vacation in a town near a major summer music venue when we were somewhat startled by the number of tourists in their 70s, 80s, and older walking extremely slowly, often holding onto the arm of a companion (or their walkers) for support. Indeed, one large parking lot was reserved for cars with handicap stickers, and large golf carts took their occupants to the performance hall. Was the slow, somewhat tentative movements of these concert goers and others strolling the streets of the nearby town an inevitable consequence of aging? Or was it the inevitable consequence of a sedentary, exercise-avoidant lifestyle? The decline in muscular strength, stamina, respiratory capacity, balance, and loss of muscle is a well-described consequence of aging.