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From Noise to Clarity: Building SEMamba for Speech Enhancement
Explore how STFT, MP-SENet, and Mamba turn noisy audio into clean speech — with a complete PyTorch implementation.
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Imagine joining an online meeting in a bustling café. Your voice has to compete with nearby conversations, footsteps, clattering cups, and the hiss of a coffee machine. By the time the microphone captures everything, your speech may be buried beneath layers of noise. So how can we bring your voice back into focus?
This is the goal of speech enhancement: transforming noisy speech into a signal that sounds cleaner, clearer, and more natural. Unlike active noise cancellation (ANC) in devices such as AirPods Pro, which generates approximately phase-inverted sound waves to reduce the noise reaching your ears, speech enhancement works directly on the recorded audio. It attempts to separate the speaker’s voice from the surrounding noise and reconstruct the underlying speech.
Traditional signal-processing methods can work well when noise is simple and predictable. Real-world noise, however, is rarely so cooperative — it changes constantly and often overlaps with speech. Deep neural networks offer a more flexible solution: by learning from…
