二维谱门控用于生物声学录音去噪
2-Dimensional spectral gating for denoising bioacoustics recordings
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中文总结 AI 辅助
针对生物声学录音中噪声干扰问题，提出基于多频率结构的二维谱门控改进Noisereduce，在鸟类和海洋哺乳动物录音中实现更优降噪且不牺牲速度。
中文摘要 AI 辅助
从生物声学录音中分离出叫声是许多生态学分析的前提，包括物种识别、动物交流理解以及个体或种群水平变异性研究。然而，当录音在开放环境中获取时，叫声、噪声或信噪比在不同个体、物种、环境和录音条件之间可能差异很大，这使得开发稳健且可推广的生态学分析方法变得困难。为了应对这些挑战，在下游分析之前使用降噪技术来去除噪声。诸如Noisereduce等流行方法依赖于谱门控，该方法为每个频率通道估计一个噪声阈值。我们提出对Noisereduce的进一步改进，利用大多数动物声音在多个频率上具有结构这一事实。我们将我们的方法应用于鸟类和海洋哺乳动物的录音，并表明我们对Noisereduce的扩展在空气和水下声学录音中均能改善降噪效果，且不影响预处理速度。
英文摘要
Isolating vocalizations from noise in bioacoustics recordings is a prerequisite to many ecological analyses, including species identification, animal communication understanding, and individual or population-level variability studies. However, when recordings are acquired in open environments, vocalizations, noise, or signal-to-noise ratio can vary widely across individuals, species, environment, and recording conditions, making it hard to develop robust and generalizable methods for ecological analyses. To account for these challenges, noise reduction techniques are used to remove noise before downstream analyses. Popular methods such as Noisereduce rely on spectral gating, which estimates a noise threshold for each frequency channel. We propose a further improvement to Noisereduce, leveraging the fact that most animal sounds have structure across multiple frequencies. We apply our method on bird and marine mammals recordings and show that our extension of Noisereduce leads to improved denoising in both above and under water acoustic recordings, without impacting speed of preprocessing.
发表机构
- Mila - Quebec AI Institute（米拉-魁北克人工智能研究所）
- Pioneer Centre for AI, University of Copenhagen（哥本哈根大学人工智能先锋中心）
- McGill University（麦吉尔大学）
- Stockholm University（斯德哥尔摩大学）
机构由 AI 辅助整理，请以论文原文为准。
