Spectral Subtraction

In this text, we will discuss the classic algorithms for speech enhancement and their significance. Speech enhancement is a technique used to improve the quality and intelligibility of speech signals, with broad applications in areas such as speech recognition, speech synthesis, and speech communication. In this article, we will cover several classic speech enhancement algorithms, including spectral subtraction, short-term energy and short-term average energy ratio, and deep learning approaches for speech enhancement. Implementing spectral subtraction typically involves calculating the noise spectrum during non-speech segments and subtracting it from the noisy signal spectrum in the frequency domain. Key steps often include computing the Fast Fourier Transform (FFT) of the input signal, estimating the noise magnitude spectrum, and applying over-subtraction factors to reduce musical noise artifacts. The objective of these algorithms is to eliminate noise, enhance the clarity of speech signals, and improve the accuracy of speech recognition. By understanding these classic algorithms, we can better grasp the principles and methods of speech enhancement and deliver superior performance for speech-related applications.