Overview
The IEEE ICASSP 2026 Grand Challenge invites participants to engage in a competition centered on Automatic Song Aesthetics Evaluation. This challenge is significant as it addresses the pressing need for effective evaluation metrics in the realm of generative music models, which have seen remarkable advancements in recent years.
Background & Relevance
The field of music generation has evolved dramatically with the introduction of generative models that can create songs of high quality and diversity. These advancements have led to applications ranging from virtual artists to movie dubbing. However, a critical challenge remains: how to evaluate the aesthetic quality of these generated songs. Traditional metrics often fall short in capturing human perception of music, particularly in terms of emotional expressiveness and listener enjoyment. This challenge aims to bridge the gap by encouraging the development of models that can predict human ratings based solely on audio inputs.
Key Details
- Challenge Title: Automatic Song Aesthetics Evaluation Challenge
- Registration Opens: September 01, 2025
- Train Set and Baseline System Release: September 10, 2025
- Test Set Release: November 10, 2025
- Results Submission Deadline: November 20, 2025
- Paper Submission Deadline: December 07, 2025 (2-page papers)
- Paper Acceptance Notification: January 11, 2026
- Camera-Ready Paper Submission: January 18, 2026
- Event Dates: May 4-8, 2026
- Location: Spain
- Submission Link: Challenge Submission
- Challenge Website: Automatic Song Aesthetics Evaluation Challenge
- ICASSP 2026 Website: ICASSP 2026
Eligibility & Participation
This challenge is open to researchers, practitioners, and students interested in the intersection of music, signal processing, and machine learning. Participants are encouraged to develop innovative models that align with the challenge’s objectives.
Submission or Application Guidelines
To participate, individuals must register for the challenge and submit their results by the specified deadlines. Participants are required to develop models that predict human ratings of song aesthetics based on audio data, utilizing the provided datasets and baseline systems.
Additional Context / Real-World Relevance
The challenge not only aims to establish a standardized benchmark for assessing musical aesthetics but also seeks to enhance the alignment of music generation technologies with human preferences. By integrating insights from signal processing, affective computing, and machine learning, this initiative has the potential to significantly impact how music is created and evaluated in the future.
Conclusion
The IEEE ICASSP 2026 Grand Challenge on Automatic Song Aesthetics Evaluation presents a unique opportunity for individuals in the AI and music fields to contribute to a vital area of research. Interested participants are encouraged to register, develop their models, and share their findings to advance the understanding of music aesthetics in the context of machine learning.
Category: Conferences & Workshops
Tags: music generation, signal processing, affective computing, machine learning, ICASSP, song aesthetics, evaluation metrics, audio analysis
