Problem
Research questions and friction points this paper is trying to address.
Hybrid AI Music
Synthetic Audio Detection
Stem-Agnostic
A Stem-Agnostic Approach to Hybrid AI Music Detection
🤖 AI Summary
为解决混合音乐中AI生成音频检测问题，提出了一种基于inspectrogram和Wiener滤波器的框架，通过CNN模型评估音轨是否由AI生成。
📝 Abstract
The inclusion of generative audio in the music production process has led to an increase in hybrid music tracks that blend authentic human performances with AI-generated stems, challenging traditional AI music detectors which operate in a binary setting. In this work, we propose a stem-agnostic framework for identifying synthetic audio sources within hybrid musical mixtures. We introduce the inspectrogram, a novel time-frequency representation that maps localized probabilities of synthetic content across the audio spectrum. By combining the inspectrogram with a Wiener filter estimating target stem energy dominance, a single CNN model evaluates whether the specific stem is generated. Trained on rendered hybrid mixtures and evaluated across various stem classes, our model achieves strong performance on high-frequency sources such as vocals, drums, and guitar, but struggles on the low-frequency, narrow-band bass. We conclude that the quality of separation impacts the detection accuracy and identify source separation as a primary bottleneck and a crucial direction for future research.
Innovation
Methods, ideas, or system contributions that make the work stand out.
stem-agnostic framework
inspectrogram
Wiener filter
CNN model
source separation
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