用深度学习优化音乐频谱，让耳机音乐更好掩盖环境噪音
Perceptual Noise-Masking with Music through Deep Spectral Envelope Shaping
- 基于听觉掩蔽原理，用神经网络重塑音乐频谱包络
- 在模拟场景下显著提升噪声掩蔽效果，优于现有方法
- 适合耳机降噪、音频增强等需要听觉感知优化的场景
人们常在嘈杂环境中听音乐，希望隔绝环境声。由于同时掩蔽效应，音乐信号可遮蔽部分噪声频率成分。本文提出一种基于心理声学掩蔽模型的神经网络，通过预测滤波器频率响应来重塑音乐的频谱包络，增强其掩蔽能力。模型采用感知损失函数，在有效掩蔽噪声的同时，保持原始音乐混音和用户设定的音量水平。我们在模拟用户佩戴耳机在嘈杂环境中听音乐的场景下评估该方法，基于客观指标的结果表明，该系统优于当前最先进方法。
原文摘要 · Abstract (English)
People often listen to music in noisy environments, seeking to isolate themselves from ambient sounds. Indeed, a music signal can mask some of the noise's frequency components due to the effect of simultaneous masking. In this article, we propose a neural network based on a psychoacoustic masking model, designed to enhance the music's ability to mask ambient noise by reshaping its spectral envelope with predicted filter frequency responses. The model is trained with a perceptual loss function that balances two constraints: effectively masking the noise while preserving the original music mix and the user's chosen listening level. We evaluate our approach on simulated data replicating a user's experience of listening to music with headphones in a noisy environment. The results, based on defined objective metrics, demonstrate that our system improves the state of the art.
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