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Found 20 papers, 10 papers with code
PESTO: Pitch Estimation with Self-supervised Transposition-equivariant Objective
In this paper, we address the problem of pitch estimation using Self Supervised Learning (SSL).
The Piano Inpainting Application
In this work, we present the Piano Inpainting Application (PIA), a generative model focused on inpainting piano performances, as we believe that this elementary operation (restoring missing parts of a piano performance) encourages human-machine interaction and opens up new ways to approach music composition.
CRASH: Raw Audio Score-based Generative Modeling for Controllable High-resolution Drum Sound Synthesis
In this paper, we propose a novel score-base generative model for unconditional raw audio synthesis.
Spectrogram Inpainting for Interactive Generation of Instrument Sounds
Modern approaches to sound synthesis using deep neural networks are hard to control, especially when fine-grained conditioning information is not available, hindering their adoption by musicians.
Incorporating Music Knowledge in Continual Dataset Augmentation for Music Generation
In this paper, we present augmentative generation (Aug-Gen), a method of dataset augmentation for any music generation system trained on a resource-constrained domain.
Vector Quantized Contrastive Predictive Coding for Template-based Music Generation
In this work, we propose a flexible method for generating variations of discrete sequences in which tokens can be grouped into basic units, like sentences in a text or bars in music.
Schoenberg-Rao distances: Entropy-based and geometry-aware statistical Hilbert distances
Distances between probability distributions that take into account the geometry of their sample space, like the Wasserstein or the Maximum Mean Discrepancy (MMD) distances have received a lot of attention in machine learning as they can, for instance, be used to compare probability distributions with disjoint supports.
A note on the quasiconvex Jensen divergences and the quasiconvex Bregman divergences derived thereof
We then define the strictly quasiconvex Bregman divergences as the limit case of scaled and skewed quasiconvex Jensen divergences, and report a simple closed-form formula which shows that these divergences are only pseudo-divergences at countably many inflection points of the generators.
NONOTO: A Model-agnostic Web Interface for Interactive Music Composition by Inpainting
Inpainting-based generative modeling allows for stimulating human-machine interactions by letting users perform stylistically coherent local editions to an object using a statistical model.
Neural Drum Machine : An Interactive System for Real-time Synthesis of Drum Sounds
In this work, we introduce a system for real-time generation of drum sounds.
Learning to Traverse Latent Spaces for Musical Score Inpainting
The designed model takes both past and future musical context into account and is capable of suggesting ways to connect them in a musically meaningful manner.
On power chi expansions of $f$-divergences
We consider both finite and infinite power chi expansions of $f$-divergences derived from Taylor's expansions of smooth generators, and elaborate on cases where these expansions yield closed-form formula, bounded approximations, or analytic divergence series expressions of $f$-divergences.
Variation Network: Learning High-level Attributes for Controlled Input Manipulation
This paper presents the Variation Network (VarNet), a generative model providing means to manipulate the high-level attributes of a given input.
Monte Carlo Information Geometry: The dually flat case
When equipping a statistical manifold with the KL divergence, the induced manifold structure is dually flat, and the KL divergence between distributions amounts to an equivalent Bregman divergence on their corresponding parameters.
Interactive Music Generation with Positional Constraints using Anticipation-RNNs
We demonstrate its efficiency on the task of generating melodies satisfying positional constraints in the style of the soprano parts of the J. S.
Deep Learning Techniques for Music Generation -- A Survey
Examples are: scalar, one-hot or many-hot.
Deep rank-based transposition-invariant distances on musical sequences
These musical sequences belong to a given corpus (or style) and it is obvious that a good distance on musical sequences should take this information into account; being able to define a distance ex nihilo which could be applicable to all music styles seems implausible.
Information Retrieval Sound
GLSR-VAE: Geodesic Latent Space Regularization for Variational AutoEncoder Architectures
VAEs (Variational AutoEncoders) have proved to be powerful in the context of density modeling and have been used in a variety of contexts for creative purposes.
DeepBach: a Steerable Model for Bach Chorales Generation
This paper introduces DeepBach, a graphical model aimed at modeling polyphonic music and specifically hymn-like pieces.
Style Imitation and Chord Invention in Polyphonic Music with Exponential Families
Modeling polyphonic music is a particularly challenging task because of the intricate interplay between melody and harmony.
