An Intelligent Interface for Drum Pattern Variation and Comparative Evaluation of Algorithms

Vogl, R.; Leimeister, Matthias; Nuanáin, Cárthach Ó; al., et

doi:10.17743/jaes.2016.0016

Cited by 8 publications

(2 citation statements)

References 7 publications

(10 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the other hand, quantitative user studies from music editors' view are rarely seen. Related studies are mostly non-quantitative expert interviews [20,37]. In a recent study which evaluates the quality of automatic music transcription method for ethnomusicologists [18], the correlation between the transcription time and user-reported transcription effort is reported, and the results indicate the potential to perform quantitative editing tests for music generation.…”

Section: Related Workmentioning

confidence: 99%

Actions Speak Louder than Listening: Evaluating Music Style Transfer based on Editing Experience

Lu,

Wu,

Chiu

et al. 2021

Preprint

View full text Add to dashboard Cite

The subjective evaluation of music generation techniques has been mostly done with questionnaire-based listening tests while ignoring the perspectives from music composition, arrangement, and soundtrack editing. In this paper, we propose an editing test to evaluate users' editing experience of music generation models in a systematic way. To do this, we design a new music style transfer model combining the non-chronological inference architecture, autoregressive models and the Transformer, which serves as an improvement from the baseline model on the same style transfer task. Then, we compare the performance of the two models with a conventional listening test and the proposed editing test, in which the quality of generated samples is assessed by the amount of effort (e.g., the number of required keyboard and mouse actions) spent by users to polish a music clip. Results on two target styles indicate that the improvement over the baseline model can be reflected by the editing test quantitatively. Also, the editing test provides profound insights which are not accessible from usual listening tests. The major contribution of this paper is the systematic presentation of the editing test and the corresponding insights, while the proposed music style transfer model based on state-of-the-art neural networks represents another contribution. CCS CONCEPTS• Applied computing → Sound and music computing; • Humancentered computing → Empirical studies in HCI.

show abstract

Section: Related Workmentioning

confidence: 99%

Actions Speak Louder than Listening: Evaluating Music Style Transfer based on Editing Experience

Lu,

Wu,

Chiu

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Hellmer et al [15] introduced methods to quantify the measures of microtiming of a drum track, which is also adopted in this work. Although not performance-related, there exist other works on drums, such as beat generation [16,17], drum sound synthesis [18,19] and interface design [20,21].…”

Section: Related Workmentioning

confidence: 99%

PocketVAE: A Two-step Model for Groove Generation and Control

Lee¹,

Kim²,

Nam³

2021

Preprint

View full text Add to dashboard Cite

Creating a good drum track to imitate a skilled performer in digital audio workstations (DAWs) can be a timeconsuming process, especially for those unfamiliar with drums. In this work, we introduce PocketVAE, a groove generation system that applies grooves to users' rudimentary MIDI tracks, i.e, templates. Grooves can be either transferred from a reference track, generated randomly or with conditions, such as genres. Our system, consisting of different modules for each groove component, takes a two-step approach that is analogous to a music creation process. First, the note module updates the user template through addition and deletion of notes; Second, the velocity and microtiming modules add details to this generated note score. In order to model the drum notes, we apply a discrete latent representation method via Vector Quantized Variational Autoencoder (VQ-VAE), as drum notes have a discrete property, unlike velocity and microtiming values. We show that our two-step approach and the usage of a discrete encoding space improves the learning of the original data distribution. Additionally, we discuss the benefit of incorporating control elements -genre, velocity and microtiming patterns -into the model.

show abstract

Generating Drum Rhythms Through Data-Driven Conceptual Blending of Features and Genetic Algorithms

Καλιακάτσος-Παπακώστας

2018

Computational Intelligence in Music, Sound, Art and Design

View full text Add to dashboard Cite

An Intelligent Interface for Drum Pattern Variation and Comparative Evaluation of Algorithms

Cited by 8 publications

References 7 publications

Actions Speak Louder than Listening: Evaluating Music Style Transfer based on Editing Experience

Actions Speak Louder than Listening: Evaluating Music Style Transfer based on Editing Experience

PocketVAE: A Two-step Model for Groove Generation and Control

Generating Drum Rhythms Through Data-Driven Conceptual Blending of Features and Genetic Algorithms

Contact Info

Product

Resources

About