4 accepted papers
Recently, research on audio foundation models has witnessed notable advances, as illustrated by the ever improving results on complex downstream tasks. Subsequently, those pretrained networks have quickly been used for various audio applications. These improvements have however resulted in a conside…
Despite significant advances in deep models for music generation, the use of these techniques remains restricted to expert users. Before being democratized among musicians, generative models must first provide expressive control over the generation, as this conditions the integration of deep generat…
Deep learning models are now core components of modern audio synthesis, and their use has increased significantly in recent years, leading to highly accurate systems for multiple tasks. However, this quest for quality comes at a tremendous computational cost, which incurs vast energy consumption and…
Audio synthesizers are pervasive in modern music production. These highly complex audio generation functions provide a unique diversity through their large sets of parameters. However, this feature also can make them extremely hard and obfuscated to use, especially for non-expert users with no forma…