The recent developments in the field of music technology and information retrieval showcase a significant shift towards leveraging advanced machine learning models for creative and analytical tasks. A notable trend is the integration of hierarchical attention mechanisms and self-supervised learning techniques to enhance the alignment and generation capabilities of music from various inputs, such as video and text. This approach not only improves the quality and diversity of generated music but also addresses the challenge of evaluating these outputs in a manner that aligns with human perception. Furthermore, the exploration of Large Language Models (LLMs) in music information retrieval tasks indicates a promising direction for automating complex music analysis tasks, despite the challenges posed by inherent biases in these models.

Noteworthy Papers

• GVMGen: Introduces a model for generating music from video inputs with high correspondence and diversity, utilizing hierarchical attentions for feature alignment.
• Towards An Integrated Approach for Expressive Piano Performance Synthesis from Music Scores: Presents a system that transforms music scores into expressive piano performances, combining Transformer-based models with neural MIDI synthesis.
• MusicEval: Proposes a novel dataset and evaluation model for text-to-music systems, aiming to align automatic assessments with human perception.
• S-KEY: Extends self-supervised learning techniques for tonality estimation, enabling the distinction between major and minor keys without human annotation.
• Exploring GPT's Ability as a Judge in Music Understanding: Demonstrates the potential of LLMs in music information retrieval tasks, highlighting their ability to detect errors in music annotations.
• Musical ethnocentrism in Large Language Models: Investigates geocultural biases in LLMs, revealing a preference for Western music cultures in model outputs.