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 landscape of AI-generated audio, the line between genuine and fabricated sound is blurring at an alarming rate. This technological advancement, while impressive, brings with it a host of security challenges, from misinformatio
Depression is a widespread mental health disorder, and recent advancements in AI are offering new ways to detect and assess it. Researchers have been leveraging multi-modal data—such as speech, video, and transcripts—to develop AI-assisted depression
Implicit Neural Representations (INRs) have revolutionized how we handle signals like images, audio, and 3D scenes. These frameworks, including MLPs with Fourier features, SIREN, and multiresolution hash grids, have been incredibly powerful. However,
Imagine a world where artificial intelligence doesn’t just mimic the brain but actually aligns with it, creating audio representations that are eerily similar to how our own brains process sound. This isn’t a distant dream but a reality, according to
Imagine capturing sound with pinpoint accuracy, even when using a compact microphone array. This isn’t a futuristic dream but a reality, thanks to a groundbreaking approach called Neural Directional Filtering (NDF). Researchers Weilong Huang, Srikant
Imagine a world where machines can seamlessly understand the intent behind every word spoken in a lengthy, multi-speaker conversation. This isn’t a distant dream, but a reality that’s inching closer, thanks to the innovative work of researchers like
In the ever-evolving landscape of audio technology, achieving diverse and high-quality audio transformations from text prompts has been a persistent challenge. Traditional methods have been hampered by their dependence on a limited set of differentia
In the ever-evolving landscape of audio technology, achieving diverse and high-quality audio transformations from text prompts has been a persistent challenge. Traditional methods have been constrained by their dependence on a limited set of differen
In the rapidly evolving landscape of artificial intelligence, multimodal large language models (MLLMs) have emerged as a focal point of research, showcasing remarkable advancements in scale and capabilities. However, the intelligence, limitations, an
