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基于小波的鸟鸣去噪

Birdsong Denoising Using Wavelets.

作者信息

Priyadarshani Nirosha, Marsland Stephen, Castro Isabel, Punchihewa Amal

机构信息

School of Engineering and Advanced Technology, Massey University, Palmerston North, New Zealand.

Institute of Agriculture & Environment, Massey University, Palmerston North, New Zealand.

出版信息

PLoS One. 2016 Jan 26;11(1):e0146790. doi: 10.1371/journal.pone.0146790. eCollection 2016.

Abstract

Automatic recording of birdsong is becoming the preferred way to monitor and quantify bird populations worldwide. Programmable recorders allow recordings to be obtained at all times of day and year for extended periods of time. Consequently, there is a critical need for robust automated birdsong recognition. One prominent obstacle to achieving this is low signal to noise ratio in unattended recordings. Field recordings are often very noisy: birdsong is only one component in a recording, which also includes noise from the environment (such as wind and rain), other animals (including insects), and human-related activities, as well as noise from the recorder itself. We describe a method of denoising using a combination of the wavelet packet decomposition and band-pass or low-pass filtering, and present experiments that demonstrate an order of magnitude improvement in noise reduction over natural noisy bird recordings.

摘要

自动记录鸟鸣声正成为全球监测和量化鸟类种群的首选方式。可编程录音机能够在一年中的任何时候、一天中的任何时段进行长时间录音。因此,对于强大的自动鸟鸣声识别技术有着迫切需求。实现这一目标的一个突出障碍是无人值守录音中的低信噪比。野外录音通常噪声很大:鸟鸣声只是录音中的一个组成部分,录音中还包括来自环境的噪音(如风雨声)、其他动物(包括昆虫)、与人类相关的活动产生的噪音,以及录音机本身产生的噪音。我们描述了一种结合小波包分解和带通或低通滤波的去噪方法,并展示了相关实验,这些实验表明与自然嘈杂鸟类录音相比,降噪效果有一个数量级的提升。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5a92/4728069/bc463dc8a726/pone.0146790.g001.jpg

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