If there was something that always frustrated me was not fully understanding Bayesian inference. Sometime last year, I came across an article about a TensorFlow-supported R package for Bayesian analysis, called greta. Back then, I searched for greta tutorials and stumbled on this blog post that praised a textbook called Statistical Rethinking: A Bayesian Course with Examples in R and Stan by Richard McElreath. I had found a solution to my lingering frustration so I bought a copy straight away. I spent the last few months reading it cover to cover and solving the proposed exercises, which are heavily based on the rethinking package. I cannot recommend it highly enough to whoever seeks a solid grip on Bayesian statistics, both in theory and application. This post ought to be my most gratifying blogging experience so far, in that I am essentially reporting my own recent learning. I am convinced this will make the storytelling all the more effective. As a demonstration, the female cuckoo reproductive output data recently analysed by Riehl et al., 2019 [1] will be modelled using In the process, we will conduct the MCMC sampling, visualise posterior distributions, generate predictions and ultimately assess the influence of social parasitism in female reproductive output. You should have some familiarity with standard statistical models. If you need to refresh some basics of probabilities using R have a look into my first post. I hope you enjoy as much as I did!

Spotify is jumping into the voice technology game. The streaming platform is testing voice-controlled advertisements for some users in the U.S., it revealed on a call with investors this week, according to TechCrunch. Currently, the promotions are only for services inside its own app, but that could change over time. Spotify is jumping into the voice technology game. The firm is testing voice-controlled advertisements for some users in the U.S., it revealed on a call with investors this week Here's how the technology works: When non-Premium users are listening to a song, they'll hear an advertisement for a playlist.

We propose a completely unsupervised method to understand audio scenes observed with random microphone arrangements by decomposing the scene into its constituent sources and their relative presence in each microphone. To this end, we formulate a neural network architecture that can be interpreted as a nonnegative tensor factorization of a multi-channel audio recording. By clustering on the learned network parameters corresponding to channel content, we can learn sources' individual spectral dictionaries and their activation patterns over time. Our method allows us to leverage deep learning advances like end-to-end training, while also allowing stochastic minibatch training so that we can feasibly decompose realistic audio scenes that are intractable to decompose using standard methods. This neural network architecture is easily extensible to other kinds of tensor factorizations.

Self-supervised learning uses way more supervisory signals than supervised learning, and enormously more than reinforcement learning. That's why calling it "unsupervised" is totally misleading. That's also why more knowledge about the structure of the world can be learned through self-supervised learning than from the other two paradigms: the data is unlimited, and amount of feedback provided by each example is huge.

What will storytelling look like in 20 years? Will it still be on your television? Will it printed on paper or projected in 3D? Prophesying the future is hard. But, like fortune telling with tea leaves, sometimes the future can be glimpsed in what's here right now. Last year, Charlie Brooker's Black Mirror: Bandersnatch – a nihilistic choose-your-own-adventure style film with five main endings – introduced Netflix viewers to a term that has only recently entered the TV lexicon: interactive storytelling.

Recommender systems are personalized information access applications; they are ubiquitous in today's online environment, and effective at finding items that meet user needs and tastes. As the reach of recommender systems has extended, it has become apparent that the single-minded focus on the user common to academic research has obscured other important aspects of recommendation outcomes. Properties such as fairness, balance, profitability, and reciprocity are not captured by typical metrics for recommender system evaluation. The concept of multistakeholder recommendation has emerged as a unifying framework for describing and understanding recommendation settings where the end user is not the sole focus. This article describes the origins of multistakeholder recommendation, and the landscape of system designs. It provides illustrative examples of current research, as well as outlining open questions and research directions for the field.

In this work, we address the problem of musical timbre transfer, where the goal is to manipulate the timbre of a sound sample from one instrument to match another instrument while preserving other musical content, such as pitch, rhythm, and loudness. In principle, one could apply image-based style transfer techniques to a time-frequency representation of an audio signal, but this depends on having a representation that allows independent manipulation of timbre as well as high-quality waveform generation. We introduce TimbreTron, a method for musical timbre transfer which applies "image" domain style transfer to a time-frequency representation of the audio signal, and then produces a high-quality waveform using a conditional WaveNet synthesizer. We show that the Constant Q Transform (CQT) representation is particularly well-suited to convolutional architectures due to its approximate pitch equivariance. Based on human perceptual evaluations, we confirmed that TimbreTron recognizably transferred the timbre while otherwise preserving the musical content, for both monophonic and polyphonic samples.

Personalized recommendation algorithms learn a user's preference for an item by measuring a distance/similarity between them. However, some of the existing recommendation models (e.g., matrix factorization) assume a linear relationship between the user and item. This approach limits the capacity of recommender systems, since the interactions between users and items in real-world applications are much more complex than the linear relationship. To overcome this limitation, in this paper, we design and propose a deep learning framework called Signed Distance-based Deep Memory Recommender, which captures non-linear relationships between users and items explicitly and implicitly, and work well in both general recommendation task and shopping basket-based recommendation task. Through an extensive empirical study on six real-world datasets in the two recommendation tasks, our proposed approach achieved significant improvement over ten state-of-the-art recommendation models.

A D-Wave quantum annealer (QA) having a 2048 qubit lattice, with no missing qubits and couplings, allowed embedding of a complete graph of a Restricted Boltzmann Machine (RBM). A handwritten digit OptDigits data set having 8x7 pixels of visible units was used to train the RBM using a classical Contrastive Divergence. Embedding of the classically-trained RBM into the D-Wave lattice was used to demonstrate that the QA offers a high-efficiency alternative to the classical Markov Chain Monte Carlo (MCMC) for reconstructing missing labels of the test images as well as a generative model. At any training iteration, the D-Wave-based classification had classification error more than two times lower than MCMC. The main goal of this study was to investigate the quality of the sample from the RBM model distribution and its comparison to a classical MCMC sample. For the OptDigits dataset, the states in the D-Wave sample belonged to about two times more local valleys compared to the MCMC sample. All the lowest-energy (the highest joint probability) local minima in the MCMC sample were also found by the D-Wave. The D-Wave missed many of the higher-energy local valleys, while finding many "new" local valleys consistently missed by the MCMC. It was established that the "new" local valleys that the D-Wave finds are important for the model distribution in terms of the energy of the corresponding local minima, the width of the local valleys, and the height of the escape barrier.

Personal use of this material is permitted. Abstract--Given the recent surge in developments of deep x learning, this article provides a review of the state-of-the-art input sequence deep learning techniques for audio signal processing. Subsequently, prominent deep learning application areas are covered, i.e. audio recognition (automatic The number of labels to be predicted (left), and the type of each label (right). While many deep learning methods have been adopted from I. INTRODUCTION Audio [2] in 1986, and finally 3) the success of deep learning in signals are commonly transformed into two-dimensional timefrequency speech recognition [3] and image classification [4] in 2012, representations for processing, but the two axes, leading to a renaissance of deep learning, involving e.g. Images are instantaneous snapshots networks (CNNs, [6]) and long short-term memory (LSTM, of a target and often analyzed as a whole or in patches [7]). In this "deep" paradigm, architectures with a large number with little order constraints; however audio signals have to be of parameters are trained to learn from a massive amount of studied sequentially in chronological order. METHODS many areas of signal processing, often outperforming traditional To set the stage, we give a conceptual overview of audio signal processing on a large scale. In this most recent analysis and synthesis problems (II-A), the input representations wave, deep learning first gained traction in image processing commonly used to address them (II-B), and the models [4], but was then widely adopted in speech processing, music shared between different application fields (II-C). H. Purwins is with Department of Architecture, Design & Media Technology, This division encompasses two independent axes (cf. Manuscript received October 11, 2018 While the audio signal will often be processed into a sequence of features, This is a PREPRINT we consider this part of the solution, not of the task. JOURNAL OF SELECTED TOPICS OF SIGNAL PROCESSING, VOL.