Audio style transfer using shallow convolutional networks and random filters

Chen, Jiyou; Yang, Gaobo; Zhao, Haitao; Ramasamy, Manimaran

doi:10.1007/s11042-020-08798-6

Cited by 10 publications

(6 citation statements)

References 14 publications

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“…(5) Constantly update the particle velocity and position parameters according to equations ( 2)-( 4). ( 6) e end of the specified condition is reached, otherwise it goes back to step (2). e algorithm flow is shown in Figure 5.…”

Section: Complexitymentioning

confidence: 99%

“…Music visualization is one of the expressions of music iconography that focuses on music feature extraction, emotion detection, and image processing [1,2]. Visualization is a form of data expression and delivery, similar to the way computers simulate the way people express emotions that can be seen through expressions, body movements, and so forth.…”

Section: Introductionmentioning

confidence: 99%

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[Retracted] Visual Classification of Music Style Transfer Based on PSO‐BP Rating Prediction Model

2021

Complexity

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In this paper, based on computer reading and processing of music frequency, amplitude, timbre, image pixel, color filling, and so forth, a method of image style transfer guided by music feature data is implemented in real-time playback, using existing music files and image files, processing and trying to reconstruct the fluent relationship between the two in terms of auditory and visual, generating dynamic, musical sound visualization with real-time changes in the visualization. Although recommendation systems have been well developed in real applications, the limitations of CF algorithms are slowly coming to light as the number of people increases day by day, such as the data sparsity problem caused by the scarcity of rated items, the cold start problem caused by new items and new users. The work is dynamic, with real-time changes in music and sound. Taking portraits as an experimental case, but allowing users to customize the input of both music and image files, this new visualization can provide users with a personalized service of mass customization and generate personalized portraits according to personal preferences. At the same time, we take advantage of the BP neural network’s ability to handle complex nonlinear problems and construct a rating prediction model between the user and item attribute features, referred to as the PSO-BP rating prediction model, by combining the features of global optimization of particle swarm optimization algorithm, and make further improvements based on the traditional collaborative filtering algorithm.

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Section: Complexitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

[Retracted] Visual Classification of Music Style Transfer Based on PSO‐BP Rating Prediction Model

2021

Complexity

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“…Moreover, the deviation of the musicality of protein music from that of specific music style may be further reduced by the advanced artificial intelligence methods. Deep learning algorithms for recomposing the style of one audio data using the style of another have been developed [44] (arXiv:1711.11160v1). These algorithms may be potentially extended for the calibration of our derived protein music towards the Fantasy-Impromptu style or any other targeted style.…”

Section: Discussionmentioning

confidence: 99%

Protein music of enhanced musicality by music style guided exploration of diverse amino acid properties

Tay

Liu

Wang

et al. 2021

Heliyon

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Inspired by the traceable analogies between protein sequences and music notes, protein music has been composed from amino acid sequences for popularizing science and sourcing melodies. Despite the continuous development of protein-to-music algorithms, the musicality of protein music lags far behind human music. Musicality may be enhanced by fine-tuned protein-to-music mapping to the features of a specific music style. We analyzed the features of a music style (Fantasy-Impromptu style), and used the quantized musical features to guide broad exploration of diverse amino acid properties (104 properties, sequence patterns and variations) for developing a novel protein-to-music algorithm of enhanced musicality. This algorithm was applied to 18 proteins of various biological functions. The derived music pieces consistently exhibited enhanced musicality with respect to existing protein music. Music style guided exploration of diverse amino acid properties enable protein music composition of enhanced musicality, which may be further developed and applied to a wider variety of music styles.

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“…There are several initial attempts at audio style transfer [99,100,45,101]. In a blog, authors have recommended the use of shallow networks instead of deep pre-trained PAPERS networks such as in the case of image processing [102].…”

Section: Deep Learning For Texture Synthesismentioning

confidence: 99%

Trends in Audio Texture Analysis, Synthesis, and Applications

Sharma¹,

Umapathy²

2022

J. Audio Eng. Soc.

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Audio signals are classified into speech, music, and environmental sounds. From the evolution of audio features, an adequate amount of work has been seen in speech and music processing. On the other hand, the environmental sounds have not been studied that much, and the major reason behind it is the lack of coherent information present in an environmental sound compared with the speech signal or a musical sound. The definition to express audio textures is imprecise and insufficient, so audio textures tend to be defined by drawing a comparison to the known sound source (e.g., "it sounds like a motor" or "like a fan"). Audio textures could be either natural or artificial. Natural audio textures, such as heavy rain, fire, and stream flowing, are very common. The artificial audio textures include sounds such as applause, a motor running, someone walking on gravel, babble, and many more. Although these audio textures have been used in virtual reality, music, screen saver sounds, and more, a considerable amount of possible work is still untouched. The aim of this study is to summarize the literature on audio textures, textural features, and their applications. In this survey, the texture synthesis and features are explained in detail.

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Audio style transfer using shallow convolutional networks and random filters

Cited by 10 publications

References 14 publications

[Retracted] Visual Classification of Music Style Transfer Based on PSO‐BP Rating Prediction Model

[Retracted] Visual Classification of Music Style Transfer Based on PSO‐BP Rating Prediction Model

Protein music of enhanced musicality by music style guided exploration of diverse amino acid properties

Trends in Audio Texture Analysis, Synthesis, and Applications

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