Draft:Audio Denoising
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Audio denoising is the process of removing backgound noise from audio signals while preserving the desired sound, such as speech or music.[1]It is a key technique in signal processing and is widely applied in fields including telecommunications, music production, broadcasting, podcasting, and assistive listening devices.[2]
| Submission declined on 24 January 2026 by SomeoneDreaming (talk). This draft does not have sufficient content to warrant a standalone article of its own, but it could be merged into the existing article at Noise reduction#In audio.
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Comment: Might be AI-generated —pythoncoder (talk | contribs) 03:51, 19 January 2026 (UTC)
Audio denoising techniques range from traditional mathematical models or signal processing methods to modern machine learning-based approaches.[3]
Background
Audio signals often contain background noise introduced during recording, podcasting, or playback. Common sources of noise include environmental sounds, electrical interference, microphone artifacts, and compression-related distortions.
In the context of signal processing, audio denoising is considered a specialized application of noise reduction, focusing specifically on audio data.
Due to the complexity of speech processes and the unknown nature of the non-speech material, a particularly challenging but common form of the problem is the underdetermined case of single-channel speech denoising. Since audio material contains a high density of data samples, the complexity is further compounded by the nature of the data.[4]
Traditional Methods & Modern Machine Learning-based Approach
Traditional methods rely on mathematical models and signal processing techniques to reduce noise.[5] Spectral subtraction is a method which estimates the noise spectrum and subtracts it from the noisy signal. Wiener filtering is another classical method that minimizes the mean-square error between the estimated clean signal and the true signal.
The modern machine learning-based approach is a data-driven way, which has particularly been proposed to achieve audio denoising. Convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer-based architectures are commonly used in modern denoising pipelines.[2]
These machine learning-based approaches are particularly effective without professional skills in complex acoustic environments where traditional methods may be useless.[6]
Recently, researchers have explored deep neural networks as an alternative to traditional signal processing-based denoising techniques. These machine learning-based approaches learn to map noisy audio signals to cleaner output and have shown effectiveness compared with traditional methods. Such approaches aim at fully leveraging the expressive power of deep networks while avoiding expensive time-frequency transformations or loss of phase information.[7]The developments have contributed to the practical implementation of audio denoising systems, applications or browser-based tools in the real world.[8]
Applications
Audio denoising is applied in multiple domains, including
Terminology and Usage
In consumer software and marketing contexts, tools designed for audio denoising are sometimes referred to as audio cleaner or voice cleaner.[10] However, the term is informal and not commonly used in academic or technical literature.