Convolutional Encoder–Decoder Architecture for Speech Enhancement

Signal processing faces the quandary of not being able to separate non-stationary noise from speech signal. Traditional methodologies relied on spectral subtraction for the same; however, such techniques relied on approximation of spectral mask of the noise. This paper proposes an effective and novel convolutional encoder–decoder architecture to effectuate clean speech from the input audio through denoising the audio input. The architecture uses skip connections to increase information flow from encoder to decoder, which helped the authors bolster the performance of the network. The generated output is evaluated on objective and subjective metrics like signal-to-noise ratio (SDR), perceptual evaluation of speech quality (PESQ) and short time objective intelligibility (STOI). The proposed system outperforms the state-of-the-art systems with respect to SDR, PESQ and STOI. The architecture finds applications in various fields such as speech recognition, machine translation and telecommunication.