Improved CEM for Speech Harmonic Enhancement in Single Channel Noise Suppression

Abstract

The periodic nature of voiced speech is often exploited to restore speech harmonics and to increase interharmonic noise suppression. In particular, a recent paper proposed to do this by manipulating the speech harmonic frequencies in the cepstral domain. The manipulations were carried out on the cepstrum of the excitation signal, obtained by the sourcefilter decomposition of speech. This method was termed Cepstral Excitation Manipulation (CEM). In this contribution we further analyse this method, point out its inherent weakness and propose means to overcome it. First of all, it will be shown by both illustrative examples and theoretical analysis that the existing method underestimates the excitation, especially at low signal to noise ratio (SNR) conditions. This inherent weakness leads to speech harmonic weakening and vocoding due to the insufficient noise suppression in the inter-harmonic regions. Then, we propose two modifications to improve the robustness and performance of CEM in low SNR cases. The first modification is to use an instantaneous amplifying factor adapted to the signal, instead of a pre-defined constant, for the excitation cepstrum. The second modification is to smooth the excitation cepstrum to preserve additional fine structure, instead of discarding it. These modifications result in better preservation of speech harmonics, more refined fine structure and higher inter-harmonic noise suppression. Experimental evaluations using a range of standard instrumental metrics conclusively demonstrate that our proposed modifications clearly outperform the existing method, especially in extremely noisy conditions.

Investigations on the Optimal Estimation of Speech Envelopes for the Two-Stage Speech Enhancement

“…Then, according to the source-filter model, the enhanced signal is decomposed into the excitation signal and the envelope , and each component can be enhanced individually. The enhancement of the speech excitation signal has been discussed in References [ 4 , 5 , 23 ], showing that the idealised excitation signal brings the benefit of recovering the weak or lost harmonics in the initial speech estimate.…”

“…While the excitation signal can be modeled by straightforward mathematical equations due to its periodic nature in the voiced frames with the largest energy [ 4 , 5 , 23 ], data-driven methods are more common in the estimation of the speech envelopes as in References [ 10 , 11 , 12 , 13 ]. If the underlying clean-speech envelope can be accurately estimated from the distorted or noisy signal envelope, it should improve the final speech estimate.…”

“…Then, according to the source-filter model, the enhanced signal is decomposed into the excitation signal R l (m) and the envelope H l (m), and each component can be enhanced individually. The enhancement of the speech excitation signal has been discussed in References [4,5,23], showing that the idealised excitation signal R l (m) brings the benefit of recovering the weak or lost harmonics in the initial speech estimate. While the excitation signal can be modeled by straightforward mathematical equations due to its periodic nature in the voiced frames with the largest energy [4,5,23], data-driven methods are more common in the estimation of the speech envelopes as in References [10][11][12][13].…”

“…The enhancement of the speech excitation signal has been discussed in References [4,5,23], showing that the idealised excitation signal R l (m) brings the benefit of recovering the weak or lost harmonics in the initial speech estimate. While the excitation signal can be modeled by straightforward mathematical equations due to its periodic nature in the voiced frames with the largest energy [4,5,23], data-driven methods are more common in the estimation of the speech envelopes as in References [10][11][12][13]. If the underlying clean-speech envelope can be accurately estimated from the distorted or noisy signal envelope, it should improve the final speech estimate.…”

“…In our recent work [ 4 ], for example, we improve the speech harmonic recovery method termed cepstral excitation manipulation (CEM) [ 5 ] using the source-filter model of speech production to highlight its periodic structure. In this model, the speech signal is decomposed into an excitation and an envelope component in order to represent the excitation source and the vocal tract filter, respectively.…”