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Found 13 papers, 0 papers with code
Diff-A-Riff: Musical Accompaniment Co-creation via Latent Diffusion Models
Recent advancements in deep generative models present new opportunities for music production but also pose challenges, such as high computational demands and limited audio quality.
Bass Accompaniment Generation via Latent Diffusion
At the core of our method are audio autoencoders that efficiently compress audio waveform samples into invertible latent representations, and a conditional latent diffusion model that takes as input the latent encoding of a mix and generates the latent encoding of a corresponding stem.
"Melatonin": A Case Study on AI-induced Musical Style
Although the use of AI tools in music composition and production is steadily increasing, as witnessed by the newly founded AI song contest, analysis of music produced using these tools is still relatively uncommon as a mean to gain insight in the ways AI tools impact music production.
High-Level Control of Drum Track Generation Using Learned Patterns of Rhythmic Interaction
Here we propose a model for conditional kick drum track generation that takes existing musical material as input, in addition to a low-dimensional code that encodes the desired relation between the existing material and the new material to be generated.
Auto-adaptive Resonance Equalization using Dilated Residual Networks
The second component is a deep neural network that predicts the optimal attenuation factor based on the windowed audio.
A Predictive Model for Music Based on Learned Interval Representations
We show that the RGAE improves the state of the art for general connectionist sequence models in learning to predict monophonic melodies, and that ensembles of relative and absolute music processing models improve the results appreciably.
Learning Transposition-Invariant Interval Features from Symbolic Music and Audio
Many music theoretical constructs (such as scale types, modes, cadences, and chord types) are defined in terms of pitch intervals---relative distances between pitches.
Strategies for Conceptual Change in Convolutional Neural Networks
A remarkable feature of human beings is their capacity for creative behaviour, referring to their ability to react to problems in ways that are novel, surprising, and useful.
What were you expecting? Using Expectancy Features to Predict Expressive Performances of Classical Piano Music
In this paper we present preliminary work examining the relationship between the formation of expectations and the realization of musical performances, paying particular attention to expressive tempo and dynamics.
Learning Musical Relations using Gated Autoencoders
Music is usually highly structured and it is still an open question how to design models which can successfully learn to recognize and represent musical structure.
From Bach to the Beatles: The simulation of human tonal expectation using ecologically-trained predictive models
Tonal structure is in part conveyed by statistical regularities between musical events, and research has shown that computational models reflect tonal structure in music by capturing these regularities in schematic constructs like pitch histograms.
Improving Content-Invariance in Gated Autoencoders for 2D and 3D Object Rotation
Content-invariance in mapping codes learned by GAEs is a useful feature for various relation learning tasks.
Imposing higher-level Structure in Polyphonic Music Generation using Convolutional Restricted Boltzmann Machines and Constraints
We introduce a method for imposing higher-level structure on generated, polyphonic music.
