Abstract
Wind induced noise is one of the major concerns of outdoor acoustic signal acquisition. It affects many field measurement and audio recording scenarios. Filtering such noise is known to be difficult due to its broadband and time varying nature. In this paper, a new method to mitigate wind induced noise in microphone signals is developed. Instead of applying filtering techniques, wind induced noise is statistically separated from wanted signals in a singular spectral subspace. The paper is presented in the context of handling microphone signals acquired outdoor for acoustic sensing and environmental noise monitoring or soundscapes sampling. The method includes two complementary stages, namely decomposition and reconstruction. The first stage decomposes mixed signals in eigen-subspaces, selects and groups the principal components according to their contributions to wind noise and wanted signals in the singular spectrum domain. The second stage reconstructs the signals in the time domain, resulting in the separation of wind noise and wanted signals. Results show that microphone wind noise is separable in the singular spectrum domain evidenced by the weighted correlation. The new method might be generalized to other outdoor sound acquisition applications.
Highlights
Recent and rapidly increasing research activities in acoustic sensing technology and acoustic signals along with Internet of Things (IoT) motivated the use of sound signatures to identify objects, sense the environmental variables and capture relevant events
In [1,2], acoustic signals are used for early fault diagnosis of bearing and stator faults of single-phase induction motor
This paper presents a new approach to wind noise reduction
Summary
Recent and rapidly increasing research activities in acoustic sensing technology and acoustic signals along with Internet of Things (IoT) motivated the use of sound signatures to identify objects, sense the environmental variables and capture relevant events. Many state of the art techniques that based on acoustic signals have been applied in many applications [1]. Acoustic signals are used in diagnostic techniques of machines in the field of industry and engineering, where many rotating machines such as electric motors are used. Diagnosis of such motors is considered as a normal maintenance process [1]. Using acoustic signals is nowadays an up-to-date method for many applications of fault diagnosis and localisation in rotating machines. In [1,2], acoustic signals are used for early fault diagnosis of bearing and stator faults of single-phase induction motor. In [3], they are used for automatic bearing fault localisation
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