User:EntryCurator/sandbox

Background

Audio signals often contain unwanted 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.

A particularly challenging but common form of the problem is the underdetermined case of single-channel speech denoising, due to the complexity of speech processes and the unknown nature of the non-speech material. The complexity is further compounded by the nature of the data, since audio material contains a high density of data samples. ^[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 commonly used 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 particularly has been introduced to enhance denoising performance. 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 fail.^[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.^[4]The developments have contributed to the practical implementation of audio denoising systems, applications or browser-based tools in the real world.^[7]

Applications

Audio denoising is applied in multiple domains, including

1. Speech enhancement for telecommunications and virtual assistants;
2. Cleaning holidays or interview recordings for important moment;^[8]
3. Broadcasting and podcast editing;
4. Music restoration and post-production;
5. Accessibility technologies such as hearing aids.^[6]

Terminology and Usage

In consumer software and marketing contexts, tools designed for audio denoising are sometimes referred to as audio cleaner or voice cleaner.^[9]. However, the term is informal and not commonly used in academic or technical literature.

References