AES Europe 2026

The rapid development of artificial intelligence
composition technology has brought innovation to music
creation. However, current deep learning music generation
models often neglect the global correlation of emotional
features, resulting in fragmented emotional expression in
generated works; insufficient alignment with human
emotional perception, making it difficult to meet the core
demand for emotional conveyance in diverse music creation.
This study aims to propose a music generation method that
integrates a global perception mechanism for emotional
features. Taking the EMOPIA; VGMIDI preprocessed
datasets as the research objects, an improved model based
on EMelodyGen (EMelodyGen-PPO) is constructed: a GLU
network layer is introduced in the feature extraction stage
to enhance the model's ability to filter; represent
emotion-related features; an improved PPO-Clip algorithm is
integrated in the training process,; a multi-dimensional
emotional reward function is designed to achieve global
dynamic perception; optimization of emotional features.
Experimental results show that the music21 parsing rate of
the EMelodyGen-PPO model on the target dataset is 3%; 4%
higher than that of the baseline model, respectively. An
automated quality assessment system based on fluency,
rhythm stability, harmony richness, melodic smoothness,;
structural integrity verifies that the comprehensive score
of the model's generated works is significantly better than
that of the comparative model. This study provides an
efficient technical path for emotion-oriented music
generation, which can empower grassroots cultural workers
; independent musicians at low cost, facilitate diverse
music creation practices; emotional audio content
dissemination,; align with the diversity; innovative
development concept of the AES audio community.