User-guided Rendering of Audio Objects Using an Interactive Genetic Algorithm

Wilson, Alex; Fazenda, Bruno

doi:10.17743/jaes.2019.0035

Cited by 3 publications

(12 citation statements)

References 11 publications

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“…For comparison purposes, SUS will be used in subsequent interpolator testing, but to probe this area further, the standard SUS questions will be augmented with additional items, more specific to the interface. This strategy has been shown to work successfully in other evaluations of audio technology interfaces [35].…”

Section: Discussionmentioning

confidence: 92%

Analyzing journeys in sound: usability of graphical interpolators for sound design

Gibson

Polfreman

2020

Pers Ubiquit Comput

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Graphical interpolation systems provide a simple mechanism for the control of sound synthesis by providing a level of abstraction above the engine parameters, allowing users to explore different sounds without awareness of the underlying details. Typically, a graphical interpolator presents the user with a two-dimensional pane where a number of synthesizer presets, each representing a collection of synthesis parameter values, can be located. Moving an interpolation cursor within the pane results in the calculation of new parameter values, based on its position, the relative locations of the presets, and the mathematical interpolation function, thus generating new sounds. These systems supply users with two sensory modalities in the form of sonic output and visual feedback from the interface. A number of graphical interpolator systems have been developed over the years, with a variety of user-interface designs, but few have been subject to formal user evaluation. Our testing studied both user interaction with, and the perceived usability of, graphical interpolation systems by comparing alternative visualizations in order to establish whether the visual feedback provided by the interface aids the locating of sounds within the space. The outcomes of our study may help to better understand design considerations for graphical interpolators and inform future designs.

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Section: Discussionmentioning

confidence: 92%

Analyzing journeys in sound: usability of graphical interpolators for sound design

Gibson

Polfreman

2020

Pers Ubiquit Comput

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“…In order to verify the effectiveness of the method in this paper, our method is compared with three state-of-the-art methods [11], [14], [20]. In our method and other three comparison methods, the population size N is 150, the maximum evolutionary number T 20.…”

Section: Experimental Comparison and Analysis A Parameter Settingmentioning

confidence: 99%

“…It is further completed the fitness comparison of methods [11], [14], [20] and our method, and the experimental results are shown in Fig.9-10 and Tab.1-2. The following conclusions can be drawn from the figures and tables.…”

Section: Adaptive Fitness Comparisonmentioning

confidence: 99%

“…However, the accuracy of the discrete adaptive fitness itself was poor, and this method did not take the uncertainty of the evaluation into consideration. In methods [12]- [14], adaptive fitness assignment methods based on interval and fuzzy numbers are proposed. Compared with precise and discrete numbers, interval and fuzzy numbers can better reflect the uncertainty and progressiveness of the evaluation process, and greatly improve the accuracy of adaptive fitness.…”

Section: Introductionmentioning

confidence: 99%

“…Besides, a number of scientists have been working on the second class of methods to improve interactive genetic algorithm. On the basis of machine learning technology, a variety of interactive genetic algorithms were proposed [14], [16], [17] to predict the evaluation results by proxy model, which could reduce some of the heavy evaluation work. Machine learning technologies expand search capability, reduce operation burden and improve algorithm performance.…”

Section: Introductionmentioning

confidence: 99%

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User Preference Adaptive Fitness of Interactive Genetic Algorithm Based Ceramic Disk Pattern Generation Method

Cai

2020

IEEE Access

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Pattern design in real life is a complex optimization challenge that objective function can not express explicitly, and can be classified as an implicit target optimization problem. Owing to the fact that the objective function in implicit target optimization problem can not be completely structured, the traditional intelligent decision optimization method is inapplicable. This paper proposes an interactive genetic algorithm based on user preference adaptive fitness, in which users actively participate in optimization and take their subjective evaluation value as the adaptive fitness of evolutionary individuals. On the basis of adjusting the tone coordination degree of ceramic disc pattern, the improved method gives the calculation of user interest degree and style similarity, and completes the establishment of user preference model. The individual similarity calculation of the algorithm integrates the user preference information, making the calculation result more objective. In the experiment, the grain genes of ceramic disc pattern can be divided into two types: main body and border, both of which can be encoded by 16-bit binary code strings. And the weight values of style, tone collocation and structure were set respectively, and the ceramic disc patterns of modern fashion and fresh styles were obtained. In addition, the algorithm performance comparison is also completed. The experimental results show that with the progress of evolution, the midpoint and average width of the fitness of the population decrease, and the population quality is improved. Our algorithm can generate ceramic disc patterns of different styles, and the algorithm can obtain more satisfactory solutions in less evolutionary generations, which speeds up the algorithm's convergence speed and shows better evolutionary optimization ability. INDEX TERMS Interactive genetic algorithm, user preferences, fitness, ceramic, design of disc. HUAFEI CAI received the M.S. and Ph.D. degrees from the

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Automated Personalized Loudness Control for Multi-Track Recordings

Moroșanu,

Negru,

Paleologu

2024

Algorithms

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This paper presents a novel approach to automated music mixing, focusing on the optimization of loudness control in multi-track recordings. By taking into consideration the complexity and artistic nature of traditional mixing processes, we introduce a personalized multi-track leveling method using two types of approaches: a customized genetic algorithm and a neural network-based method. Our method tackles common challenges encountered by audio professionals during prolonged mixing sessions, where consistency can decrease as a result of fatigue. Our algorithm serves as a ‘virtual assistant’ to consistently uphold the initial mixing objectives, hence assuring consistent quality throughout the process. In addition, our system automates the repetitive elements of the mixing process, resulting in a substantial reduction in production time. This enables engineers to dedicate their attention to more innovative and intricate jobs. Our experimental framework involves 20 diverse songs and 10 sound engineers possessing a wide range of expertise, offering a useful perspective on the adaptability and effectiveness of our method in real-world scenarios. The results demonstrate the capacity of the algorithms to mimic decision-making, achieving an optimal balance in the mix that resonates with the emotional and technical aspects of music production.

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User-guided Rendering of Audio Objects Using an Interactive Genetic Algorithm

Cited by 3 publications

References 11 publications

Analyzing journeys in sound: usability of graphical interpolators for sound design

Analyzing journeys in sound: usability of graphical interpolators for sound design

User Preference Adaptive Fitness of Interactive Genetic Algorithm Based Ceramic Disk Pattern Generation Method

Automated Personalized Loudness Control for Multi-Track Recordings

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