In lung sound research, low-frequency noise usually disturbs the sound signal being recorded. Some researchers therefore use high-pass filtration before the final analysis. In this study, the effect of digital and analog high-pass filtration on the morphology of the lung sound crackles is evaluated. The original nonprefiltered crackle waveform is presented, and the effect of the high-pass filtration on the crackle waveform characteristics is elucidated in one patient with silicoasbestosis.
We present two improved objective measures and one new measure for the characterization of the effect of noise suppression (NS) algorithms. These metrics are an evolution of earlier work [5]. The Signal-to-Noise Ratio Improvement ( S N N measure describes the capability of an NS method to enhance the speech component of a noisy speech signal from an additive background noise. The Noise Power Level Reduction (NPLR) measure indicates the NS impact on the noise level in the vicinity of speech. In this paper, we enbance the formulation of SNRI and NPLR by replacing frame powers by their logarithms to improve the robusmess of the memcs and to increase the association with subjective perception. A new thud measure is the Difleerence between SNRl and NPLR (DSN) that monitors the consistency of an NS solution in attenuating the background noise while causing a minimal effect on the speech level. The earlier metrics presented in [5] are included as characterization tools in recent 3GPPl [I] and TIA2 [6] minimum performance requirement specifications for NS algorithms in mobile terminals. The enhanced and new measures are proposed for a draft ITU-T recommendation G.VED, "Voice Enhancement Devices" [Z]. In this paper, we present the revised formulation of the measures as well as results of a verification study, including a regression analysis between the presented measures and listening test results. The analysis suggests that the objective measures are relevant for the perception of overall acceptability of the effect of an NS algorithm.
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