Search Results for author:
Found 16 papers, 3 papers with code
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.
Singer Identity Representation Learning using Self-Supervised Techniques
Significant strides have been made in creating voice identity representations using speech data.
PESTO: Pitch Estimation with Self-supervised Transposition-equivariant Objective
In this paper, we address the problem of pitch estimation using Self Supervised Learning (SSL).
Exploring Sampling Techniques for Generating Melodies with a Transformer Language Model
To accomplish this, we train a high-capacity transformer model on a vast collection of highly-structured Irish folk melodies and analyze the musical qualities of the samples generated using distribution truncation sampling techniques.
On the Typicality of Musical Sequences
It has been shown in a recent publication that words in human-produced English language tend to have an information content close to the conditional entropy.
SampleMatch: Drum Sample Retrieval by Musical Context
Artists must use their aesthetic judgement to assess whether a given drum sample fits the current musical context.
DrumGAN VST: A Plugin for Drum Sound Analysis/Synthesis With Autoencoding Generative Adversarial Networks
In contemporary popular music production, drum sound design is commonly performed by cumbersome browsing and processing of pre-recorded samples in sound libraries.
VQCPC-GAN: Variable-Length Adversarial Audio Synthesis Using Vector-Quantized Contrastive Predictive Coding
Influenced by the field of Computer Vision, Generative Adversarial Networks (GANs) are often adopted for the audio domain using fixed-size two-dimensional spectrogram representations as the "image data".
Modeling Musical Structure with Artificial Neural Networks
Furthermore, I motivate the relevance of musical transformations in structure modeling and show how a connectionist model, the Gated Autoencoder (GAE), can be employed to learn transformations between musical fragments.
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.
Learning Complex Basis Functions for Invariant Representations of Audio
Mapping signals onto complex basis functions learned by the CAE results in a transformation-invariant "magnitude space" and a transformation-variant "phase space".
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.
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.
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.
