The matching of the soundtrack in a movie or a video can have an enormous influence in the message being con- veyed and its impact, in the sense of involvement and engagement, and ultimately in their aesthetic and entertainment qualities. Art is often associated with creativity, implying the presence of inspiration, originality and appropriateness. Evolutionary systems provides us with the novelty, showing us new and subtly different solutions in every generation, possibly stimulating the creativity of the human using the system. In this paper, we present Genetic Soundtracks, an evolutionary approach to the creative matching of audio to a video. It analyzes both media to extract features based on their content, and adopts genetic algorithms, with the purpose of truncating, combining and adjusting audio clips, to align and match them with the video scenes. Index Terms— Genetic algorithms, multimedia, entertainment, feature extraction, audio & video signal processing, video editing
Evolutionary computation has been widely used in art domains, such as music generation @cite_9 and video generation @cite_1 . @cite_0 , music videos are automatically generated from personal home videos, based on the extraction and matching of temporal structures of video and music, using genetic algorithms to find global optimal solutions. These solutions may involve repetitive patterns in video based on those found in the music. In MovieGene @cite_4 the authors used genetic algorithms to explore creative editing and production of videos, with the main focus on visual and semantic properties, by defining criteria or interactively performing selections in the evolving population of video clips, which could be explored and discovered through emergent narratives and aesthetics.
@cite_0 @cite_9 @cite_1 @cite_4
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We propose a new multimedia authoring paradigm based on evolutionary computation, video annotation, and cinematic rules. New clips are produced in an evolving population through genetic transformations influenced by user choices, and regulated by cinematic techniques like montage and video editing. The evolutionary mechanisms, through the fitness function will condition how video sequences are retrieved and assembled, based on the video annotations. The system uses several descriptors, as genetic information, coded in an XML document following the MPEG-7 standard. With evolving video, the clips can be explored and discovered through emergent narratives and aesthetics in ways that inspire creativity and learning about the topics that are presented. The boundaries of art are subjective, but the impetus for art is often associated with creativity, regarded with wonder and admiration along human history. Most interesting activities and their products are a result of creativity. The main goal of our approach is to explore new creative ways of editing and producing videos, using evolutionary algorithms. A creative evolutionary system makes use of evolutionary computation operators and properties and is designed to aid our own creative processes, and to generate results to problems that traditionally required creative people to solve. Our system is able to generate new videos or to help a user in doing so. New video sequences are combined and selected, based on their characteristics represented as video annotations, either by defining criteria or by interactively performing selections in the evolving population of video clips, in forms that can reflect editing styles. With evolving video, the clips can be explored through emergent narratives and aesthetics in ways that may reveal or inspire creativity in digital art. Generating music has a few notable differences from generating images and videos. First, music is an art of time, necessitating a temporal model. Second, music is usually composed of multiple instruments tracks, with close interaction with one another. Each track has its own temporal dynamics, but collectively they unfold over time interdependently. Lastly, for symbolic domain music generation, the targeted output is sequences of discrete musical events, not continuous values. In this paper, we propose and study three generative adversarial networks (GANs) for symbolic-domain multi-track music generation, using a data set of 127,731 MIDI bars of pop rock music. The three models, which differ in the underlying model assumption and accordingly the network architecture, are referred to as the jamming model, composer model, and hybrid model, respectively. We propose a few intra-track and inter-track objective metrics to examine and compare their generation result, in addition to a subjective evaluation. We show that our models can learn from the noisy MIDI files and generate coherent music of four bars right from scratch (i.e. without human inputs). We also propose extensions of our models to facilitate human-AI cooperative music creation: given the piano track composed by human we can generate four additional tracks in return to accompany it. We present an Evolutionary Visual Exploration (EVE) system that combines visual analytics with stochastic optimisation to aid the exploration of multidimensional datasets characterised by a large number of possible views or projections. Starting from dimensions whose values are automatically calculated by a PCA, an interactive evolutionary algorithm progressively builds (or evolves) non-trivial viewpoints in the form of linear and non-linear dimension combinations, to help users discover new interesting views and relationships in their data. The criteria for evolving new dimensions is not known a priori and are partially specified by the user via an interactive interface: (i) The user selects views with meaningful or interesting visual patterns and provides a satisfaction score. (ii) The system calibrates a fitness function (optimised by the evolutionary algorithm) to take into account the user input, and then calculates new views. Our method leverages automatic tools to detect interesting visual features and human interpretation to derive meaning, validate the findings and guide the exploration without having to grasp advanced statistical concepts. To validate our method, we built a prototype tool (EvoGraphDice) as an extension of an existing scatterplot matrix inspection tool, and conducted an observational study with five domain experts. Our results show that EvoGraphDice can help users quantify qualitative hypotheses and try out different scenarios to dynamically transform their data. Importantly, it allowed our experts to think laterally, better formulate their research questions and build new hypotheses for further investigation.