Traditional audio noise reduction algorithms such as spectral subtraction, Wiener filtering, adaptive filtering, and NMF are based on mathematical models of the signal and noise. Deep learning introduced a different
CNN for Audio Denoising: How Convolutional Neural Networks Enhance Speech Read More »
Traditional audio noise reduction methods such as spectral subtraction, Wiener filtering, adaptive filtering, and NMF have been widely used in digital audio systems. However, real-world acoustic environments are becoming increasingly
DNN for Speech Enhancement: How Deep Neural Networks Reduce Audio Noise Read More »
Noise reduction is not always a simple matter of subtracting a noise spectrum from an audio signal. In many real-world environments, the microphone captures a mixture of speech, background noise,
NMF for Audio Noise Reduction: How Non-Negative Matrix Factorization Works Read More »
Traditional audio noise-reduction methods often analyze a signal in either the time domain or frequency domain. But real-world audio is more complicated. Speech contains rapidly changing transients, harmonics, pauses, consonants,
Wavelet Denoising for Audio: How Wavelet Noise Reduction Works Read More »
Background noise is rarely constant. A microphone may encounter changing fan noise, vehicle noise, machinery, keyboard sounds, vibration, or other interference. When the noise characteristics change over time, a fixed
Adaptive Noise Cancellation: LMS, NLMS and RLS Explained Read More »
Wiener filtering is one of the most important classical techniques in audio noise reduction and speech enhancement. Unlike simple frequency-domain noise suppression methods, Wiener filtering attempts to estimate the relationship
Wiener Filtering for Audio Noise Reduction: Principles and Practical Applications Read More »
Spectral subtraction is one of the classic techniques used in digital audio noise reduction and speech enhancement. The basic idea is simple: estimate the noise spectrum and subtract it from
Spectral Subtraction for Audio Noise Reduction: How It Works Read More »
Audio signals contain information in both time and frequency. Understanding how this information is represented is fundamental to modern audio engineering, from MEMS microphone systems and speech enhancement to noise
FFT and STFT in Audio Signal Processing: A Practical Engineering Guide Read More »
Clear audio is not simply a matter of using a more sensitive microphone. In real-world applications, the captured signal often contains background noise, electrical noise, reverberation, interference, and unwanted sounds
Audio Noise Reduction: A Complete Guide to DSP, AI, and Microphone Array Methods Read More »
When a smart device needs to hear a person several meters away, simply increasing microphone gain is not enough. The system needs to determine which direction the desired speech is
How Does a Beamforming Microphone Array Work? A Technical Guide for Product Engineers Read More »
