This is happening tomorrow from 9am to 12 pm and 1 pm-4 pm in East Ballroom C! You can come and interact with our model too!! 🎤 Hope to see you there :) #NeurIPS2024

Work done with @huangcza at @Mila_Quebec and Université de Montréal!

While the model presents exciting directions for creative explorations and human-AI partnerships, this work notes the experiences of musicians to inform the model's future development! More information on the generative model:

We studied some musicians interacting with GaMaDHaNi, a generative model for Hindustani vocal music 🎤! I am so excited to present this work at @NeurIPSConf @ML4CDworkshop this week! 📝 https://t.co/4UAZxcA4Uo 🎧 https://t.co/VpHooj9f3B An example interaction with the model:

I'm going to be at ISMIR @ISMIRConf in SF from Nov 10-14! Looking forward to chat and discuss research with friends, old and new 🥳 #ISMIR2024

Work done with the dream team @maneeshadkr @wuyusongwys @huangcza @antoine_caillon!!

Intending to use this model for interactive human-AI generation, we present two possible use cases with our model: (1) primed generation and (2) coarse pitch conditioning. Learn more and play around with these interactions in our demo ( https://t.co/4luZu4vT9d)!

As opposed to coarse discrete note-like representations used in previous work, our fine pitch representation is more equipped to model the note ornamentations inherent in Hindustani music.

We develop GaMaDHaNi, a 2-stage hierarchical model capable of singing the intricate melodic patterns in Hindustani Classical music. Our approach models a finely quantized pitch contour which is converted to a spectrogram, and finally into audio through a vocoder.

We built a hierarchical generative model to sing Hindustani vocal melodies 🎤! We will be presenting this work at ISMIR 2024! @ISMIRConf 📝Paper: https://t.co/SMZ1DK9mZ7 💻Code: https://t.co/yrE7S6fNtN 👩🏽‍💻Demo: https://t.co/4luZu4vT9d 🎧Samples: https://t.co/5eKhMJkNCp

This looks so cool! Excited to play around with this soon 🥳 Just fyi, seems like this video on the website doesn't have audio enabled @pika_labs @demi_guo_

🚨Understanding In-Context Learning: 1. Pretrained LLMs can implement learning algorithms to learn from data in-context. 2. Transformers can encode multiple algorithms for the same task and use one based on context at inference time. 3. Attention-free models also exhibit ICL.

Tap along and let me know how (not so) annoying you find it to be 😌

Today @snpranav and I got into an argument over how frustrating it would be for the speed of a song to constantly change as a function of time. To settle it, I decided to use Bespoke to control the speed of 'Spain' by Chick Corea based on a modified sine wave :)

Last week, @imnotfady and I built KarenAI for the @bitcmp hackathon. KarenAI - your personal Karen that can help you dispute your credit card transactions, get sh*t done with customer support, and let you speak with a manager at the touch of a button using Ai! 🧵👇

Come come come, it'll be fun!!

Audio transcription models like @openai's Whisper and @google's speech-to-text are popular tools for converting spoken words into written text. But how do we compare these models and determine which one is the best? 🧵👇 (1/5)

ISMIR Awards! This year's Best Special Paper Award is for: Martin Clayton, Preeti Rao, Nithya Shikarpur, Sujoy Roychowdhury, and Jin Li for "Raga Classification from Vocal Performances Using Multimodal Analysis"

@_akhaliq This is so cool 🤯 https://t.co/1v0TbJaxCU

Thanks for all your wishes!!