Spectral local harmonicity feature for voice activity detection

In this paper, we propose a method to exploit the harmonicity of human voiced speech using only the most harmonic sub-part of the spectrum. This technique searches for all the potential sub-windows of the spectrum, and measures their local harmonicity, using a newly proposed metric, which works in the spectral autocorrelation domain and employs a novel sinusoidal fitting approach. Experiments show that the new feature can be used to detect noisy voiced speech frames heavily corrupted by non-stationary noise even at 0dB SNR with high precision and recall, which gives better results than the Windowed Autocorrelation Lag Energy (WALE), a recently proposed voicing features, under a complex factory noise scenarios.