Tag: machine learning

Workshop: Practical Machine Learning – Embed a Generative AI Model in Your App – by @dynamic_cast – ADC 2024

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https://audio.dev/ -- @audiodevcon​
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Workshop: Practical Machine Learning - Embed a generative AI model in your app and train your own interactions with it - Anna Wszeborowska, Harriet Drury, Sohyun Im, Julia Läger & Pauline Nemchak - ADC 2024
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In this workshop we’ll explore the fundamentals of Machine Learning. We will run through an easy to follow machine learning model that will:

Be easy for beginners
Run on the CPU
Be real time

This will cover an intro to Machine Learning, small vs large models and an introduction to a training environment in python. We aim to make this workshop as interactive as possible, with the idea of having a trained model in session for everyone to use/play with.

This will be a self-contained workshop aiming to be accessible to all levels of learning - all elements used in the practical part of the workshop will be thoroughly explained in the introduction.
Dynamic Cast - Who Are We?
Dynamic Cast is a peer-to-peer C++ study group, a safe space for underrepresented groups (women, LGBTQIA+, minority ethnic). The Dynamic Cast workshop at ADC is designed to create an entry point to the industry for newcomers, everyone is welcome.
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Slides: https://data.audio.dev/talks/2024/practical-machine-learning/slides.pdf
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Anna Wszeborowska

Anna is an independent software developer and consultant working on interactive real-time systems for music and audio. She is also an academic researcher focusing on exploring strategies for aiding musical self-expression with machine learning. During her time at Ableton she held the role of Technical Principal for Max for Live, worked on the hardware instrument Push and contributed to some of the flagship instruments available in the DAW Live and iOS app Note. Anna has founded programming initiatives helping people underrepresented in tech advance in the field. Currently co-organises a peer-to-peer C++ study group called Dynamic Cast.
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Harriet Drury

Harriet is a Software Engineer at Native Instruments, working on iZotope branded products. She has a keen interest in DSP and ML, having written a proof of concept inference engine in Cmajor. Most recent work in ML has been on real time applications of large libraries.

Plays guitar (occasionally), can hit drums sometimes on time. Harriet co-organises Dynamic Cast, a C++ learning group for underrepresented groups. There are chapters in Berlin and London, with the option to join online, too.
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Sohyun Im

Sohyun Im graduated with a Bachelor's degree in Sound Engineering from the University of West London and is currently pursuing her Master's in Sound and Music Computing at Queen Mary University of London.

She has a keen interest in audio programming and DSP, having conducted research on Virtual Analog Modelling, which bridges the analog and digital realms, during her undergraduate studies. Additionally, she is deeply interested in the emerging field of generative music AI and is dedicated to advancing her knowledge in this area.

Sohyun is also a lifelong pianist. Regardless of the genre, feel free to invite her whenever you need a pianist.
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Julia Läger

Julia is a Software Developer with 7+ years experience writing C++ production code, working previously in automotive and now in music tech at Focusrite. But she also really likes Python. She's currently working on internal tooling, which involves a potpourri of domains and technologies, going from high-level desktop applications down to embedded libraries. She's passionate about music and science, and actually has a background in experimental nano physics.
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Pauline Nemchak

A front-end engineer, music and audio industries enthusiast and linguaphile (aren't we all).
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ADC is an annual event celebrating all audio development technologies, from music applications and game audio to audio processing and embedded systems. ADC’s mission is to help attendees acquire and develop new audio development skills, and build a network that will support their audio developer career.
Annual ADC Conference - https://audio.dev/
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Streamed & Edited by Digital Medium Ltd: https://online.digital-medium.co.uk
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Organized and produced by JUCE: https://juce.com/
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Special thanks to the ADC24 Team:

Sophie Carus
Derek Heimlich
Andrew Kirk
Bobby Lombardi
Tom Poole
Ralph Richbourg
Jim Roper
Jonathan Roper
Prashant Mishra

#machinelearning #machinelearningapplications #ai #adc #audiodev #audio #machinelearningwithpython #audioprocessing #audioproduction #audioprogramming #musictech #soundtech #audiotech #audiotechnology

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Inference Engines and Audio – Harriet Drury – ADC23

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https://audio.dev/ -- @audiodevcon​

Inference Engines and Audio - Harriet Drury - ADC 2023

Machine learning has become a buzzword in recent years, but how does it actually work? This talk aims to introduce and explain inference pipelines. We’ll look at commonly used inference engines, how they work, their suitability for use in audio applications, and how to go about creating your own.

Also introduced will be an approach to writing a custom inference engine for the Cmajor platform.

Link to Slides: https://data.audio.dev/talks/2023/inference-engines-and-audio/slides.pdf
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Harriet Drury
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Streamed & Edited by Digital Medium Ltd: https://online.digital-medium.co.uk
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Organized and produced by JUCE: https://juce.com/
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Special thanks to the ADC23 Team:

Sophie Carus
Derek Heimlich
Andrew Kirk
Bobby Lombardi
Tom Poole
Ralph Richbourg
Jim Roper
Jonathan Roper
Prashant Mishra

#adc #audio #audiotech #machinelearning

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Designing Smart Algorithms with Traditional DSP vs. Machine Learning – Amit Shoham – ADCx SF

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Join Us For ADC23 - London - 13-15 November 2023
More Info: https://audio.dev/
@audiodevcon

Designing Smart Algorithms with Traditional DSP vs. Machine Learning - Amit Shoham - ADCx SF

Smart algorithms such as wakeword detection, tempo detection, song recognition, and many others have become an integral component of countless applications. These algorithms rely on a wide range of machine learning and/or traditional DSP techniques. While machine learning techniques are now solving previously impossible problems, algorithms designed with more traditional engineering techniques often require fewer system resources and are easier to deploy. In this talk we'll compare and contrast machine learning and traditional engineering approaches, and discuss fundamental principles that will help you determine what mix of techniques is best for your application.

Slides: https://data.audio.dev/talks/ADCxSF/designing-smart-algorithms/slides.pdf
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Amit Shoham

Amit Shoham has led dual careers as a musician and engineer. His professional roles have included DSP engineer, house music producer, DJ, computer vision engineer, remixer, deep learning engineer, mastering engineer, and troublemaker. Amit is currently a senior systems architect and algorithms guru at Artiphon, where his deep expertise in algorithm design and optimization helps bring to life innovative new musical instruments.

Streamed & Edited by Digital Medium Ltd - online.digital-medium.co.uk
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Organized and produced by JUCE: https://juce.com/
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Special thanks to the ADC22 Team:

Sophie Carus
Derek Heimlich
Andrew Kirk
Bobby Lombardi
Tom Poole
Ralph Richbourg
Jim Roper
Jonathan Roper

#audiodevcon #audiodev #deeplearning

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