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
Emotion recognition in conversations is a complex task, but a new approach called Orthogonal Disentanglement with Projected Feature Alignment (OD-PFA) is making waves in the field. Developed by researchers Xinyi Che, Wenbo Wang, Jian Guan, and Qijun
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
The world of music generation is evolving rapidly, thanks to advances in generative AI. However, the reliance of many neural-based models on large datasets has raised concerns about copyright infringement and high-performance costs. Enter MusicAIR, a
The digital world is grappling with an escalating threat: audio-video (AV) forgery. It’s no longer just about human-centric deepfakes; the manipulations have become more diverse and complex, extending to natural scenes. However, the tools we’ve been
Foundation models have shown an impressive ability to learn rich, transferable representations across various data types, from images to text and audio. These representations often replace raw data in machine learning pipelines, serving as the primar
In the world of artificial intelligence, Large Language Models (LLMs) and Vision-Language Models (VLMs) have made significant strides in understanding and generating human language. However, their ability to comprehend and interpret musical notation
In the world of audio processing, bandwidth extension has long been a challenging task. It involves reconstructing the high-frequency components of an audio signal from its low-pass version. Traditional methods have seen improvements over time, but t
Deepfake audio detectors have a significant challenge: they struggle to generalize to out-of-distribution inputs. This is largely due to spectral bias, a tendency of neural networks to prioritize learning low-frequency structures over high-frequency
Creating synchronized audio-visual content has long been a formidable challenge in the realm of generative AI, particularly for open-source models. Researchers from various institutions have identified three core issues that hinder robust audio-video
