Long-Term SNR Estimation Using Noise Residuals and a Two-Stage Deep-Learning Framework

Knowing the signal-to-noise ratio of a noisy speech signal is important since it can help improve speech applications. This paper presents a two-stage approach for estimating the long-term signal-to-noise ratio (SNR) of speech signals that are corrupted by background noise. The first stage produces noise residuals from a speech separation module. The second stage then uses the residuals and a deep neural network (DNN) to predict long-term SNR. Traditional SNR estimation approaches use signal processing, unsupervised learning, or computational auditory scene analysis (CASA) techniques. We propose a deep-learning based approach, since DNNs have outperformed other techniques in several speech processing tasks. We evaluate our approach across a variety of noise types and input SNR levels, using the TIMIT speech corpus and NOISEX-92 noise database. The results show that our approach generalizes well in unseen noisy environments, and it outperforms several existing methods.