The world of artificial intelligence is abuzz with the potential of Multimodal Large Language Models (MLLMs), which combine and interpret multiple forms of data, such as text, audio, and visuals. However, a team of researchers from various institutio
In the world of multimedia production, the ability to manipulate audio with precision is paramount. Producers often need to handle audio tracks individually for editing, mixing, and creative control. However, current methods for generating audio from
In the world of music technology, the quest for expressive music performance rendering has always been a significant challenge. Traditionally, researchers have relied on supervised learning methods, which require large amounts of labeled data. Howeve
In the ever-evolving landscape of audio and biomedical signal analysis, researchers are constantly seeking innovative ways to improve the efficiency and accuracy of deep learning models. A recent study introduces the Parallel Delayed Memory Unit (PDM
In the rapidly evolving world of AI, the ability to handle and process different types of data, or modalities, is becoming increasingly important. This is where multimodal AI systems come into play, and a crucial aspect of these systems is chunking s
In the ever-evolving world of audio technology, researchers are constantly pushing the boundaries to improve the way we compress and reconstruct sound. A recent breakthrough comes from Tal Shuster and Eliya Nachmani, who have introduced a novel quant
In the ever-evolving landscape of music production, a groundbreaking development has emerged that promises to revolutionize the way we approach post-production. Researchers have introduced LLM2Fx-Tools, a multimodal tool-calling framework designed to
The world of speech synthesis is abuzz with the latest advancements in diffusion models, and for good reason. These models have been pushing the boundaries of audio quality and stability, making them a formidable force in the generative AI landscape.
In the rapidly evolving world of artificial intelligence, the quest for models that are both powerful and efficient is never-ending. Enter LFM2, a family of Liquid Foundation Models that are designed to strike a balance between these two seemingly co
In the world of signal processing, estimating parameters for complex signals is a formidable task, especially when dealing with multiple components. Traditional methods often hit a wall when the number of components grows, grappling with issues like
