Hidden Markov Models (HMMs) are a ubiquitous tool to model time series data, and have been widely used in two main tasks of Automatic Music Transcription (AMT): note segmentation, i.e. identifying the played notes after a multi-pitch estimation, and sequential post-processing, i.e. correcting note segmentation using training data. In this paper, we employ the multi-pitch estimation method called Probabilistic Latent Component Analysis (PLCA), and develop AMT systems by integrating different HMM-based modules in this framework. For note segmentation, we use two different twostate on/o? HMMs, including a higher-order one for duration modeling. For sequential post-processing, we focused on a musicological modeling of polyphonic harmonic transitions, using a first- and second-order HMMs whose states are defined through candidate note mixtures. These different PLCA plus HMM systems have been evaluated comparatively on two different instrument repertoires, namely the piano (using the MAPS database) and the marovany zither. Our results show that the use of HMMs could bring noticeable improvements to transcription results, depending on the instrument repertoire.

The task of determining item similarity is a crucial one in a recommender system. This constitutes the base upon which the recommender system will work to determine which items are more likely to be enjoyed by a user, resulting in more user engagement. In this paper we tackle the problem of determining song similarity based solely on song metadata (such as the performer, and song title) and on tags contributed by users. We evaluate our approach under a series of different machine learning algorithms. We conclude that tf-idf achieves better results than Word2Vec to model the dataset to feature vectors. We also conclude that k-NN models have better performance than SVMs and Linear Regression for this problem.

The importance of algorithms in our lives today cannot be overstated. They are used virtually everywhere, from financial institutions to dating sites. But some algorithms shape and control our world more than others -- and these ten are the most significant. Just a quick refresher before we get started. Though there's no formal definition, computer scientists describe algorithms as a set of rules that define a sequence of operations.

For me this question has always been the defining moment of Ridley Scott's sci-fi classic, Blade Runner. Deckard, the policeman anti-hero played by Harrison Ford, has just discovered that Rachel, the self-possessed personal assistant to the founder of the Tyrell Corporation, is in fact one of the company's advanced replicants: a robot. His question to Dr Eldon Tyrell is loaded with the certainty of bigotry -- that repeated'it'. But Deckard's uncertainty about Rachel, and the essential differences between humans and machines, is just the beginning of a process of disorientation that pursues him all the way to the film's brutal but surprising climax. That we have yet to reach the dark dystopia of Blade Runner in real life is pretty obvious -- just look around you. It is also obvious from the nonetheless intriguing robotics exhibition that opened a few weeks ago at the Science Museum in London.

Whenever the term "artificial intelligence" is mentioned, the first thought that comes to any layman's mind is that it is the technology which helps develop robots. Major Hollywood movies have given a superficial impression about the technological subject. But artificial intelligence is a much broader concept that the regular notion of it. If we talk about it in the literal sense, the term artificial intelligence stands for the simulation of human intelligence processed by machines, mainly computer systems. The major processes under this simulation are reasoning, learning, and self-correction. These processes are later elaborated with reference to the needs of technology.

Shoppers may not know it, but they use artificial intelligence (AI) in their everyday lives. From turning to apps like Waze for optimal driving routes to using suggested tags on Facebook photos, consumers are immersed in AI as part of their daily routines. Despite this, many retailers continue to question AI and wonder whether it's time to start investing in these emerging technologies. But if getting closer to their customers is the ultimate goal, retailers need to embrace AI today as a powerful tool to engage shoppers and supercharge employees. Armed with the ability to research products on brands' websites, social media channels and more, today's consumers are more knowledgeable than ever.

The ritualized procreation in the novel--effectively, state-sanctioned rape--is extrapolated from the Bible. " 'Behold my maid Bilhah, go in unto her; and she shall bear upon my knees, that I may also have children by her,' " Atwood recited. "Obviously, they stuck the two together and out came the baby, and it was given to Rachel.

When you move to another country as an adult, the language flows around you like a river. Perhaps a child can immediately abandon himself to the current, but most older people will begin by picking out the words and phrases that seem to matter most, which is what I did after my family moved to Cairo, in October of 2011. It was the first fall after the Arab Spring; Hosni Mubarak, the former President, had been forced to resign the previous February. Every weekday, my wife, Leslie, and I met with a tutor for two hours at a language school called Kalimat, where we studied Egyptian Arabic. At the end of each session, we made a vocabulary list. In early December, following the first round of the nation's parliamentary elections, which had been dominated by the Muslim Brotherhood, my language notebook read: On many days, I went to Tahrir Square, to report on the ongoing revolution. If I heard unfamiliar words or phrases, I brought them back to class. The following month, I learned "tear gas," "slaughter," and "Can you speak more slowly?" "Conspiracy theory" appeared in my notebook on the same day as "fried potatoes." Sometimes I wondered about the strangeness of Tahrir-speak, and what my Arabic would have been like if I had arrived ten years earlier. But it would have been different at any time, in any place: you can never step into the same language twice. Even eternal phrases took on a new texture in the light of the revolution. After I could understand some of the radio talk shows that cabbies played, I realized that callers and hosts exchanged Islamic greetings for a full half minute before settling down to heated arguments about the new regime. Our textbook was entitled "Dardasha"--"Chatter"--and it outlined set conversations that I soon carried out with neighbors, using phrases that would never be touched by Tahrir: "May peace, mercy, and the blessings of God be upon you." One of our teachers, Rifaat Amin, prepared a five-page handout entitled "Arabic Expressions of Social Etiquette." This supplemented "Dardasha," which also featured some lessons about social traditions, including the evil eye, the belief that envy can cause misfortune. In "Dardasha," icons of little bombs with burning fuses had been printed next to the kind of phrase that, even during a revolution, qualified as explosive: "Your son is really smart, Madame Fathiya."

Artificial intelligence can now judge your photos or at least give them a probability of whether or not it's an awesome photo. Just how good is that photo you just snapped? Everypixel Aesthetics thinks it may have the answer. The new neural network algorithm is designed to both auto tag and generate a probability that the photo is a good one. The tool comes from Everypixel, a startup that is looking to change the stock photography market by creating a search tool that browses multiple platforms at once, giving the little guy just as much exposure as the stock photo giants.

TristouNet is a neural network architecture based on Long Short-Term Memory recurrent networks, meant to project speech sequences into a fixed-dimensional euclidean space. Thanks to the triplet loss paradigm used for training, the resulting sequence embeddings can be compared directly with the euclidean distance, for speaker comparison purposes. Experiments on short (between 500ms and 5s) speech turn comparison and speaker change detection show that TristouNet brings significant improvements over the current state-of-the-art techniques for both tasks.