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Found 26 papers, 14 papers with code
Continuous descriptor-based control for deep audio synthesis
Despite significant advances in deep models for music generation, the use of these techniques remains restricted to expert users.
SingSong: Generating musical accompaniments from singing
We present SingSong, a system that generates instrumental music to accompany input vocals, potentially offering musicians and non-musicians alike an intuitive new way to create music featuring their own voice.
Challenges in creative generative models for music: a divergence maximization perspective
The development of generative Machine Learning (ML) models in creative practices, enabled by the recent improvements in usability and availability of pre-trained models, is raising more and more interest among artists, practitioners and performers.
Creative divergent synthesis with generative models
Machine learning approaches now achieve impressive generation capabilities in numerous domains such as image, audio or video.
Streamable Neural Audio Synthesis With Non-Causal Convolutions
As our method is based on a post-training reconfiguration of the model, we show that it is able to transform models trained without causal constraints into a streaming model.
HEAR: Holistic Evaluation of Audio Representations
The aim of the HEAR benchmark is to develop a general-purpose audio representation that provides a strong basis for learning in a wide variety of tasks and scenarios.
RAVE: A variational autoencoder for fast and high-quality neural audio synthesis
By leveraging a multi-band decomposition of the raw waveform, we show that our model is the first able to generate 48kHz audio signals, while simultaneously running 20 times faster than real-time on a standard laptop CPU.
Signal-domain representation of symbolic music for learning embedding spaces
We evaluate the ability to learn meaningful features from this representation from a musical point of view.
Energy Consumption of Deep Generative Audio Models
In most scientific domains, the deep learning community has largely focused on the quality of deep generative models, resulting in highly accurate and successful solutions.
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.
Creativity in the era of artificial intelligence
As creativity is a highly context-prone concept, we underline the limits and deficiencies of current AI, requiring to move towards artificial creativity.
Timbre latent space: exploration and creative aspects
Recent studies show the ability of unsupervised models to learn invertible audio representations using Auto-Encoders.
Neural Granular Sound Synthesis
In this setting the learned grain space is invertible, meaning that we can continuously synthesize sound when traversing its dimensions.
Ultra-light deep MIR by trimming lottery tickets
We show that our proposal can remove up to 90% of the model parameters without loss of accuracy, leading to ultra-light deep MIR models.
Diet deep generative audio models with structured lottery
This hypothesis states that extremely efficient small sub-networks exist in deep models and would provide higher accuracy than larger models if trained in isolation.
Vector-Quantized Timbre Representation
Although its definition is usually elusive, it can be seen from a signal processing viewpoint as all the spectral features that are perceived independently from pitch and loudness.
Cross-modal variational inference for bijective signal-symbol translation
Extraction of symbolic information from signals is an active field of research enabling numerous applications especially in the Musical Information Retrieval domain.
Multi-Step Chord Sequence Prediction Based on Aggregated Multi-Scale Encoder-Decoder Network
In this paper, we postulate that this comes from the multi-scale structure of musical information and propose new architectures based on an iterative temporal aggregation of input labels.
Using musical relationships between chord labels in automatic chord extraction tasks
Recent researches on Automatic Chord Extraction (ACE) have focused on the improvement of models based on machine learning.
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.
Universal audio synthesizer control with normalizing flows
By using this formulation, we show that we can address simultaneously automatic parameter inference, macro-control learning and audio-based preset exploration within a single model.
Assisted Sound Sample Generation with Musical Conditioning in Adversarial Auto-Encoders
Its training data subsets can directly be visualized in the 3D latent representation.
A database linking piano and orchestral MIDI scores with application to automatic projective orchestration
This article introduces the Projective Orchestral Database (POD), a collection of MIDI scores composed of pairs linking piano scores to their corresponding orchestrations.
Modulated Variational auto-Encoders for many-to-many musical timbre transfer
We define timbre transfer as applying parts of the auditory properties of a musical instrument onto another.
Sound Audio and Speech Processing
Generative timbre spaces: regularizing variational auto-encoders with perceptual metrics
Based on this, we introduce a method for descriptor-based synthesis and show that we can control the descriptors of an instrument while keeping its timbre structure.
Sound Audio and Speech Processing
Live Orchestral Piano, a system for real-time orchestral music generation
This paper introduces the first system for performing automatic orchestration based on a real-time piano input.
