beszédzajcsökkentés

Beszédzajcsökkentés, often translated as speech denoising or speech enhancement, refers to the process of removing unwanted background noise from a speech signal. The primary goal is to improve the intelligibility and quality of the speech, making it easier for humans or automatic speech recognition systems to understand.

The types of noise that can be present in speech signals are varied, ranging from continuous stationary noise like the hum of an air conditioner to non-stationary noise such as other voices, traffic sounds, or music. Beszédzajcsökkentés techniques aim to differentiate between the speech components and these noise components and then suppress or eliminate the noise.

Various methods exist for achieving speech denoising. Spectral subtraction is a classic approach where the estimated noise spectrum is subtracted from the noisy speech spectrum. More advanced techniques utilize statistical models, such as those found in Hidden Markov Models (HMMs) or deep learning-based approaches. Deep neural networks, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have shown remarkable success in recent years by learning complex mappings between noisy and clean speech.

The effectiveness of a particular speech denoising method is often evaluated based on objective measures like Signal-to-Noise Ratio (SNR) improvement or Perceptual Evaluation of Speech Quality (PESQ), as well as subjective listening tests. Applications of beszédzajcsökkentés are widespread, including telecommunications, hearing aids, voice assistants, and audio processing in media.