Boost audio tech with BigVGAN v2

📣 Exciting news for developers in the audio tech space 🌟 BigVGAN v2 is here to revolutionize audio synthesis, delivering unparalleled quality and speed. 🎧✨ https://nvda.ws/3MxMNw4 ✅ Top-notch Audio Quality ✅ Faster Synthesis ✅ Pre-trained Checkpoints ✅ High Sampling Rate Support Dive into the future of audio synthesis with BigVGAN v2 and create sounds that are indistinguishable from the real thing 👀🌐💡 #BigVGANv2 #GenerativeAI

Upon request, we have provided audio examples for our latest ultra-lightweight model. With a model size of only 253 KB, this noise reduction model for speech can be distributed on a double density 5¼ inch floppy disk from the 8088 era. The latency is only 32 milliseconds. Here's the link to the audio demo: https://hance.ai/minimodel

ML-based noise cancellation typically involves training models on large datasets containing both clean and noisy audio. These models learn to identify and suppress noise while preserving the original sound. Compared to traditional noise reduction techniques, machine learning offers adaptive solutions that improve over time and work in a variety of noisy environments. If you want a more detailed explanation, you can either ask about the technology or view the video to get insights into how ML-driven noise cancellation is applied to improve audio experiences. https://lnkd.in/dUnRwP5a #NoiseCancellation #MachineLearning #MLAudio #AudioTechnology #SuperiorAudio #SoundQuality #AIForAudio #AIInnovation #ClearAudio #TechForGood #SmartAudio #AudioEngineering #AIInAudio

Can you repeat that, please? 📱 📞 Even today communication fails during phone calls because of bad audio. The reasons can be many: wind blowing into the microphone, noise in the background, and bad reception - to name a few. With AI audio enhancement we can combat these challenges by removing noise, and even regenerating lost audio. Today HANCE can do: - Realtime noise removal - Realtime reverb removal - Realtime stem separation ... and we're just getting started.

First experiment with #AIvideo, starting with the image generated by #MidJourney, the audio generated by #ElevenLabs, the video generated by #Hedra, and upscaling + frame interpolation done using #TopazLabs.

🎵 Sound the trumpets! 🎺 Our latest EngiSphere article is music to our ears! 🎧 Stanford researchers have composed a masterpiece with their new Audio Transformer architecture, outperforming traditional CNNs in large-scale audio understanding. It's like giving AI perfect pitch! 🎼🤖 Dive into our latest blog post to discover how this groundbreaking approach is amplifying the potential of audio technology. From voice assistants to music recommendations, the future is sounding sweeter than ever! 🍯 💬 Discussion Question: How do you think Audio Transformers could change your daily interactions with sound-based technology? #AudioAI #MachineLearning #TechInnovation #StanfordResearch

Bark.cpp generates audio tokens in just 7ms! Initially, the 4-bit quantized Bark model produced tokens in 25ms. Switching to #SunoAi's small Bark model reduced inference times to 7ms while maintaining great audio quality!

audioXpress highlights Ambiq's Neural Network Speech Enhancement (NNSE) AI model as part of their "Adaptive Audio: Artificial Intelligence for Smart Hearables" report. NNSE effectively extracts clear speech from noisy backgrounds on the device and in real-time, helping advance adaptive audio applications using AI on local devices. Read the report below: