AES Technical Committee

Machine Learning & Artificial Intelligence

Chair:    M. Nyssim Lefford      Send Email
Chair:    Gordon Wichern      Send Email
Vice Chair:    Christian Uhle      Send Email
Vice Chair:    Brecht De Man      Send Email

The Technical Committee on Machine Learning and Artificial Intelligence (TC-MLAI) focuses on applications of machine learning and artificial intelligence in audio, with discussions on topics such as: best practices, data, licensing, social and cultural aspects, technical innovations, and ethics. The goal of the committee is to drive discussion and exchange information by organizing and disseminating workshops, symposia, tutorials, and technical documents. The TC-MLAI acts as a point of contact and a bridge to other AES technical committees, the AES community at large, and other organizations involved in ML and AI for audio.


Areas of Concentration

  • Artificial intelligence and creativity
  • Ethical and social considerations of AI applications.
  • Best practices for data and metadata handling, e.g. data collection, labeling, and storage.
  • Best practices for workflows, e.g. when updating, deploying and assuring quality of trained models.
  • Licensing issues for data, pretrained models, and software implementations (external libraries)
  • Aspects of evaluation, both objective and perceptual
  • Explaining and understanding decisions of data driven systems
  • Security and robustness
  • Machine Learning and Artificial Intelligence Algorithms
  • Documentation of evolution and history of ML and AI
  • Applications in:
  • Content analysis
  • Source separation
  • Music and sound generation
  • Automated and assistive audio production
  • Digital audio effects
  • Repair, restoration, and enhancement
  • Educational tools
  • Automatic speech recognition
  • Speech synthesis
  • Spatial audio
  • Audio coding
  • Systems diagnostics

Current Areas Of Work

  • Papers sessions
  • Workshops
  • Symposiums
  • Identify trends


Meeting Report:

These documents do not necessarily express the official position of the AES on the issues discussed at these meetings, and only represent the views of committee members participating in the discussion. Any unauthorized use of these publications is prohibited. Authorization must be obtained from the Executive Director of the AES: Email, Tel: +1 212 661 8528, Address: 551 Fifth Ave., Suite 1225, New York, New York 10176, USA.

2024-11-13     TC MLAI Meeting Minutes 2024-10-24
Description: TC MLAI Meeting Minutes 2024-10-24

2024-11-13     TC MLAI Meeting Minutes 2024-06-20
Description: TC MLAI Meeting Minutes 2024-06-20

2023-12-10     TC MLAI Meeting Minutes 2023-11-08
Description: TC MLAI Meeting Minutes 2023-11-08

2023-6-2     TC MLAI Meeting Minutes 2023-05-23
Description: TC MLAI Meeting Minutes 2023-05-23

2022-11-1     TC MLAI Meeting Minutes 2022-10-24
Description: TC MLAI Meeting Minutes 2022-10-24

2022-6-2     TC MLAI Meeting Minutes 2022-06-01
Description: TC MLAI Meeting Minutes 2022-06-01

2021-11-22     TC MLAI Meeting Minutes 2021-10-19
Description: TC MLAI Meeting Minutes 2021-10-19

2021-6-7     TC MLAI Meeting Minutes 2021-06-02
Description: TC MLAI Meeting Minutes 2021-06-02


Technical Report:

2024-3-20     AES155 - AI Town Hall Summary Report
Description: Report on lessons learned and potential next steps.


Other:

2024-10-25     AES157 - Copying and Attributing Training Data in Audio Generative Models (AES157)
Description: Slides of the TC-MLAI sponsored workshop at AES NY.

2024-10-25     AES157 - Upmix and Format Conversion of Multichannel Audio – an Opportunity for AI-Based Breakthroughs? (AES157)
Description: Slides of the workshop at AES NY, co-sponsored by TC-Spatial Audio and TC-MLAI.

2023-12-10     AES155 - AI Town Hall Presented by TC-MLAI (AES155)
Description: Slides from the TC-MLAI Town Hall event at AES NY.

2023-11-17     AES155 - AI for Multitrack Music Mixing (AES155)
Description: Slides of the TC-MLAI sponsored workshop at AES NY.

2023-9-25     AES 155 - AI Townhall Pre-event Resources
Description: List of resources compiled to provide a basic primer of some of the issues and positions surrounding the use of AI in creative work.

2022-11-11     AES 153 - Teaching AI to hear like we do: psychoacoustics in machine learning slides
Description: Slides from the first in-person workshop organized by TC-MLAI


Committee Members

 Brent Harshbarger  Gerald Schuller  Gordon Reid 
 Marina Bosi  Steve Hutt  David Andrews 
 Anibal Ferreira  Jean-Marc Jot  M. Nyssim Lefford 
 J. Keith McElveen  Ronny Andersson  Arijit Biswas 
 Jan Skoglund  Michelle Daniels  Renato de Castro Rabelo Profeta 
 Jonathan Wyner  Jamie Angus  Annika Neidhardt 
 Teri Grossheim  Alexander Wankhammer  Christian Uhle 
 Christos Chousidis  Bradford Swanson  Brecht De Man 
 Dave Moffat  Joseph Colonel  Katarzyna Sochaczewska 
 Christian Steinmetz  Ken Felton  Gordon Wichern 
 Jorge Garcia  Angeliki Mourgela  Pablo Delgado 
 Mikus Salgravis  Flavio Everardo  David Prince 
 Shahan Nercessian  Julian Parker  Dylan Flesch 
 Joey Stuckey  Rebecca Fiebrink  Matthew Pitkin 
 Soumya Sai Vanka  Thaddeus Páez  Brian Ellis 
 Vlad Baran  Akito van Troyer  Nikhil Singh 
 Hervé Bouley  Franco Santiago Caspe  Farida Yusuf 
 Bleiz Macsen Del Sette  Santiago Ruiz  Lillia Betz 
 James Bradbury  Hidde de Jong  Andy Sarroff 
 Gavin Barrett-Hayes  Emilia Stefanowska 

To request membership in this Technical Committee please email the Chair by using the link above.

AES - Audio Engineering Society