Theory of Colour Harmony and Its Application

The colour represents an essential element of visual and graphic communications. It plays an important role in the perception of visual design and it is significant for all participants in the process of planning, developing and promoting graphic products. Designers are interested in a psychological and presentational aspect of colours, while to the technologists the colour represents one of the most important quality attributes. The process of choosing colours that are harmonious, usable and efficient is complex. In addition, many designers have inadequate background knowledge of colour theory, which could help them with the selection of colours. As a result, designers usually spend a great deal of time and expend significant effort in choosing appropriate colour combinations. In this article, the importance of colour harmony and its application when extracting colours, rating and generating colour schemes is presented

Quantifying aesthetics of visual design applied to automatic design

In today\u27s Instagram world, with advances in ubiquitous computing and access to social networks, digital media is adopted by art and culture. In this dissertation, we study what makes a good design by investigating mechanisms to bring aesthetics of design from realm of subjection to objection. These mechanisms are a combination of three main approaches: learning theories and principles of design by collaborating with professional designers, mathematically and statistically modeling good designs from large scale datasets, and crowdscourcing to model perceived aesthetics of designs from general public responses. We then apply the knowledge gained in automatic design creation tools to help non-designers in self-publishing, and designers in inspiration and creativity. Arguably, unlike visual arts where the main goals may be abstract, visual design is conceptualized and created to convey a message and communicate with audiences. Therefore, we develop a semantic design mining framework to automatically link the design elements, layout, color, typography, and photos to linguistic concepts. The inferred semantics are applied to a design expert system to leverage user interactions in order to create personalized designs via recommendation algorithms based on the user\u27s preferences

16th Sound and Music Computing Conference SMC 2019 (28–31 May 2019, Malaga, Spain)

The 16th Sound and Music Computing Conference (SMC 2019) took place in Malaga, Spain, 28-31 May 2019 and it was organized by the Application of Information and Communication Technologies Research group (ATIC) of the University of Malaga (UMA). The SMC 2019 associated Summer School took place 25-28 May 2019. The First International Day of Women in Inclusive Engineering, Sound and Music Computing Research (WiSMC 2019) took place on 28 May 2019. The SMC 2019 TOPICS OF INTEREST included a wide selection of topics related to acoustics, psychoacoustics, music, technology for music, audio analysis, musicology, sonification, music games, machine learning, serious games, immersive audio, sound synthesis, etc

Deep Learning Techniques for Music Generation -- A Survey

This paper is a survey and an analysis of different ways of using deep learning (deep artificial neural networks) to generate musical content. We propose a methodology based on five dimensions for our analysis: Objective - What musical content is to be generated? Examples are: melody, polyphony, accompaniment or counterpoint. - For what destination and for what use? To be performed by a human(s) (in the case of a musical score), or by a machine (in the case of an audio file). Representation - What are the concepts to be manipulated? Examples are: waveform, spectrogram, note, chord, meter and beat. - What format is to be used? Examples are: MIDI, piano roll or text. - How will the representation be encoded? Examples are: scalar, one-hot or many-hot. Architecture - What type(s) of deep neural network is (are) to be used? Examples are: feedforward network, recurrent network, autoencoder or generative adversarial networks. Challenge - What are the limitations and open challenges? Examples are: variability, interactivity and creativity. Strategy - How do we model and control the process of generation? Examples are: single-step feedforward, iterative feedforward, sampling or input manipulation. For each dimension, we conduct a comparative analysis of various models and techniques and we propose some tentative multidimensional typology. This typology is bottom-up, based on the analysis of many existing deep-learning based systems for music generation selected from the relevant literature. These systems are described and are used to exemplify the various choices of objective, representation, architecture, challenge and strategy. The last section includes some discussion and some prospects.Comment: 209 pages. This paper is a simplified version of the book: J.-P. Briot, G. Hadjeres and F.-D. Pachet, Deep Learning Techniques for Music Generation, Computational Synthesis and Creative Systems, Springer, 201

Visual analytics and artificial intelligence for marketing

In today’s online environments, such as social media platforms and e-commerce websites, consumers are overloaded with information and firms are competing for their attention. Most of the data on these platforms comes in the form of text, images, or other unstructured data sources. It is important to understand which information on company websites and social media platforms are enticing and/or likeable by consumers. The impact of online visual content, in particular, remains largely unknown. Finding the drivers behind likes and clicks can help (1) understand how consumers interact with the information that is presented to them and (2) leverage this knowledge to improve marketing content. The main goal of this dissertation is to learn more about why consumers like and click on visual content online. To reach this goal visual analytics are used for automatic extraction of relevant information from visual content. This information can then be related, at scale, to consumer and their decisions

Wittgenstein and the Concept of Learning in Artificial Intelligence

The object of this investigation is to analyze the application of the concept of learning to machines and software as displayed in Artificial Intelligence (AI). This field has been approached from different philosophical perspectives. AI, however, has not yet received enough attention from a Wittgensteinian angle, a gap this thesis aims to help bridge. First we describe the use of the concept of learning in natural language by means of a familiar and of a less familiar case of human learning. This is done to give us a general idea about the meaning of this concept. By building two basic machine learning algorithms, we introduce one of the technical meanings of learning in computer science, i.e. the use of this concept in machine learning. Based on a study and comparison between both uses, the one in ordinary language and the one in machine learning, we conclude that both usages exemplify one and the same family resemblance concept of learning. We apply this insight further in a critical discussion of two specific philosophical positions about the applicability of psychological or mental concepts to software and hardware, especially in AI. One of the contributions of this investigation is that the use of mental concepts concerning machines does not imply the ascription of a mind.Philosophy - Master's ThesisMAHF-FILOFILO35

PSYCHOACOUSTIC OPTIMIZATION OF THE VQ-VAE AND TRANSFORMER ARCHITECTURES FOR HUMAN-LIKE AUDITORY PERCEPTION IN MUSIC INFORMATION RETRIEVAL AND GENERATION TASKS

Despite incredible advancements in the utilization of learning-based architectures (AI) in natural language and image domains, their applicability to the domain of music has remained limited. In fact, the performance of state-of-the-art Automated Music Transcription (AMT) systems has seen only marginal improvements from novel AI architectures. Moreover, the importance of psychoacoustic perception and its incorporation into MIR systems have mostly stayed addressed, leading to shortcomings in current approaches. This thesis provides an overview of music processing and novel neural architectures, investigates the reasons behind the subpar performance achieved by their utilization in music information retrieval (MIR) tasks, and proposes several ways of adjusting both the music (data-related) pre-processing pipelines, and psychoacoustically-adjusted transformer-based model to improve the performance on MIR and AMT tasks. In particular, a new music transformer architecture is proposed, and various algorithms of music pre-processing for psychoacoustic optimization are implemented along with several adaptive models aimed at addressing the missing factor of modeling human music perception. The preliminary performance results exhibit promising outcomes, warranting the continued investigation of transformer architectures for music information retrieval applications. Several intriguing insights unveiled during the research process are discussed and presented. The thesis concludes by delineating a set of promising future research directions, paving the way for further advancements in the field of music information retrieval and generation using proposed architectures

Machine Learning Tools in the Predictive Analysis of ERCOT Load Demand Data

The electric load industry has seen a significant transformation over the last few decades, culminating in the establishment and implementation of electricity markets. This transition separates electric generation services into a distinct, more competitive sector of the industry, allowing for the introduction of greater unpredictability into the system. Forecasting power system load has developed into a core research area in power and energy demand engineering in order to maintain a constant balance between electricity supply and demand. The purpose of this thesis dissertation is to reduce power system uncertainty by improving forecasting accuracy through the use of sophisticated machine learning techniques. Additionally, this research provides sophisticated machine learning-based forecasting methodologies for the three forecasting professions from a variety of perspectives, incorporating several advanced deep learning features such as Naïve/default, Hyperparameter Tuning, and Custom Early Stopping. We begin by creating long-term memory (LSTM) and gated recurrent unit (GRU) models for ERCOT demand data, and then compare them to some of the most well-known supervised machine learning models, such as ARIMA and SARIMA, to identify the best set of models for long- and short-term load forecasting. We will also use multiple comparison approaches, such as the radar chart and the Pygal radar chart, to perform a thorough evaluation of each of the deep learning models before settling on the best model