基于多任务预训练的情感音乐生成方法OA

AI-based affective music generation(AI-AMG)enables flexible music creation tailored to environmental contexts and listeners' physiological or emotional states,aiming to stimulate and regulate affective responses.It demonstrates strong potential in domains such as mental health therapy,immersive multimedia experiences,and intelligent music composition.Challenges remain,including the scarcity of affective music datasets,the difficulty of emotion annotation,the limited perceptual capability of existing models,and low accuracy in affective expression due to ineffective learning paradigms.A unified framework for affective music understanding and generation is proposed by integrating multi-task pre-training with fine-tuning.During the pre-training phase,proxy tasks,including bar-level attribute masking,mode prediction,and melody completion are designed to associate emotional characteristics with structural musical features using large-scale unlabeled data.Task-specific attention mechanisms enable joint learning of musical and affective representations.In the fine-tuning phase,an octuple representation incorporating valence-arousal dimensions is introduced,allowing emotion-controllable music generation on annotated datasets via a sequence-to-sequence attention mechanism.Experimental results show that the method improves four-class emotion classification accuracy by 4.2 percentage points and achieves gains of 17.48 and 3.40 percentage points in two-class classification tasks compared with baseline model.Subjective evaluation confirms enhanced perceived audio quality,and ablation experiments further validate the effectiveness of each pre-training sub-task for sentiment modeling.