Repeated exposure to violence and abusive content in music and song content can influence listeners' emotions and behaviours, potentially normalising aggression or reinforcing harmful stereotypes. In this study, we explore the use of generative artificial intelligence (GenAI) and Large Language Models (LLMs) to automatically transform abusive words (vocal delivery) and lyrical content in popular music. Rather than simply muting or replacing a single word, our approach transforms the tone, intensity, and sentiment, thus not altering just the lyrics, but how it is expressed. We present a comparative analysis of four selected English songs and their transformed counterparts, evaluating changes through both acoustic and sentiment-based lenses. Our findings indicate that Gen-AI significantly reduces vocal aggressiveness, with acoustic analysis showing improvements in Harmonic to Noise Ratio, Cepstral Peak Prominence, and Shimmer. Sentiment analysis reduced aggression by 63.3-85.6\% across artists, with major improvements in chorus sections (up to 88.6\% reduction). The transformed versions maintained musical coherence while mitigating harmful content, offering a promising alternative to traditional content moderation that avoids triggering the "forbidden fruit" effect, where the censored content becomes more appealing simply because it is restricted. This approach demonstrates the potential for GenAI to create safer listening experiences while preserving artistic expression.

翻译：反复接触音乐与歌曲内容中的暴力及冒犯性内容会影响听众的情绪与行为，可能使攻击性行为正常化或强化有害的刻板印象。本研究探索利用生成式人工智能（GenAI）与大型语言模型（LLMs）自动转化流行音乐中的冒犯性词汇（人声表达）与歌词内容。我们的方法并非简单地静音或替换单个词汇，而是对语调、强度与情感进行转化，从而不仅改变歌词，更改变其表达方式。我们对四首选定英文歌曲及其转化版本进行了对比分析，通过声学与基于情感的视角评估其变化。研究结果表明，GenAI显著降低了人声的攻击性，声学分析显示谐噪比、倒谱峰突出度与振幅微扰得到改善。情感分析显示不同艺人的攻击性降低了63.3\%至85.6\%，副歌部分改善尤为显著（最高降低88.6\%）。转化后的版本在保持音乐连贯性的同时减轻了有害内容，为传统内容审核提供了一种有前景的替代方案，避免了因内容受限制反而增强吸引力的"禁果效应"。该方法证明了GenAI在保留艺术表达的同时创造更安全聆听体验的潜力。