Robust Electric Network Frequency Estimation with Rank Reduction and Linear Prediction

This article deals with the problem of Electric Network Frequency (ENF) estimation where Signal to Noise Ratio (SNR) is an essential challenge. By exploiting the low-rank structure of the ENF signal from the audio spectrogram, we propose an approach based on robust principle component analysis to get rid of the interference from speech contents and some of the background noise, which in our case can be regarded as sparse in nature. Weighted linear prediction is enforced on the low-rank signal subspace to gain accurate ENF estimation. The performance of the proposed scheme is analyzed and evaluated as a function of SNR, and the Cramér-Rao Lower Bound (CRLB) is approached at an SNR level above -10 dB. Experiments on real datasets have demonstrated the advantages of the proposed method over state-of-the-art work in terms of estimation accuracy. Specifically, the proposed scheme can effectively capture the ENF fluctuations along the time axis using small numbers of signal observations while preserving sufficient frequency precision.