High-Level Control of Drum Track Generation Using Learned Patterns of Rhythmic Interaction

Lattner, Stefan; Grachten, Maarten

doi:10.1109/waspaa.2019.8937261

Cited by 17 publications

(19 citation statements)

References 12 publications

(13 reference statements)

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“…DrumNet: A tool for creating drum tracks conditioned on existing audio tracks like guitar, bass, or keyboard tracks (Lattner and Grachten, 2019). The output adapts to the tempo and rhythm of the existing tracks, and users can explore different rhythmic variations by traversing a latent space.…”

Section: Tools Usedmentioning

confidence: 99%

On the Development and Practice of AI Technology for Contemporary Popular Music Production

Deruty

Grachten

Lattner

et al. 2022

Transactions of the International Society for Music Information Retrieval

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Although the use of AI technology for music production is still in its infancy, it has the potential to make a lasting impact on the way we produce music. In this paper we focus on the design and use of AI music tools for the production of contemporary Popular Music, in particular genres involving studio technology as part of the creative process. First we discuss how music production practices associated with those genres can differ significantly from traditional views of how a musical work is created, and how this affects AI music technology. We argue that-given the role of symbolic representations in this context, as well as the integration of composition activities with editing and mixing-audio-based AI tools are better suited to support the artist's creative workflow than purely piano-roll/MIDI-based tools. Then we give a report of collaborations with professional artists, in which we look at how various AI tools are used in practice to produce music. We identify usage patterns as well as issues and challenges that arise in practical use of the tools. Based on this we formulate some recommendations and validation criteria for the development of AI technology for contemporary Popular Music.

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Section: Tools Usedmentioning

confidence: 99%

On the Development and Practice of AI Technology for Contemporary Popular Music Production

Deruty

Grachten

Lattner

et al. 2022

Transactions of the International Society for Music Information Retrieval

Self Cite

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“…More recent works achieve a form of one-shot style transfer by conditioning on an example of the target style, e.g. to harmonize a melody in a given piano performance style [24] or to generate a new kick drum track for a given song based on patterns extracted from a different recording [25]. While the focus of our work is also on generating accompaniments, a key difference is that the input to our Grv2Grv model is a full accompaniment rather than a partial one, and the output does not retain any of the original accompaniment tracks.…”

Section: A Music Style Transformationsmentioning

confidence: 99%

Groove2Groove: One-Shot Music Style Transfer With Supervision From Synthetic Data

Cífka

Şimşekli

Richard

2020

IEEE/ACM Trans. Audio Speech Lang. Process.

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Style transfer is the process of changing the style of an image, video, audio clip or musical piece so as to match the style of a given example. Even though the task has interesting practical applications within the music industry, it has so far received little attention from the audio and music processing community. In this paper, we present Groove2Groove, a oneshot style transfer method for symbolic music, focusing on the case of accompaniment styles in popular music and jazz. We propose an encoder-decoder neural network for the task, along with a synthetic data generation scheme to supply it with parallel training examples. This synthetic parallel data allows us to tackle the style transfer problem using end-toend supervised learning, employing powerful techniques used in natural language processing. We experimentally demonstrate the performance of the model on style transfer using existing and newly proposed metrics, and also explore the possibility of style interpolation.

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“…In recent decades, models based on artificial intelligence techniques have performed impressive predictions in different knowledge fields. For example, in exact sciences (Sauceda et al, 2021;Hajibabaei and Kim, 2021;Cerioti et al, 2021;Bahlke et al, 2020;Chmiela et al, 2020;Saar et al, 2021;Artrith and Urban, 2016;Ch'ng et al, 2017;Shallue and Vanderburg, 2018;Sadowski et al, 2016), in social sciences (Ng et al, 2020;Lattner and Grachten, 2019), in technology (Bae et al, 2021;Cunneen et al, 2019;Feldt et al, 2018;Huang et al, 2014), and health sciences (Bashyam et al, 2020;Zhou et al, 2019;Themistocleous et al, 2021;Lagree et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Learning the Use of Artificial Intelligence in Heterogeneous Catalysis

Bokhimi

2021

Front. Chem. Eng.

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We describe the use of artificial intelligence techniques in heterogeneous catalysis. This description is intended to give readers some clues for the use of these techniques in their research or industrial processes related to hydrodesulfurization. Since the description corresponds to supervised learning, first of all, we give a brief introduction to this type of learning, emphasizing the variables X and Y that define it. For each description, there is a particular emphasis on highlighting these variables. This emphasis will help define them when one works on a new application. The descriptions that we present relate to the construction of learning machines that infer adsorption energies, surface areas, adsorption isotherms of nanoporous materials, novel catalysts, and the sulfur content after hydrodesulfurization. These learning machines can predict adsorption energies with mean absolute errors of 0.15 eV for a diverse chemical space. They predict more precise surface areas of porous materials than the BET technique and can calculate their isotherms much faster than the Monte Carlo method. These machines can also predict new catalysts by learning from the catalytic behavior of materials generated through atomic substitutions. When the machines learn from the variables associated with a hydrodesulfurization process, they can predict the sulfur content in the final product.

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High-Level Control of Drum Track Generation Using Learned Patterns of Rhythmic Interaction

Cited by 17 publications

References 12 publications

On the Development and Practice of AI Technology for Contemporary Popular Music Production

On the Development and Practice of AI Technology for Contemporary Popular Music Production

Groove2Groove: One-Shot Music Style Transfer With Supervision From Synthetic Data

Learning the Use of Artificial Intelligence in Heterogeneous Catalysis

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