Abstract-In this paper, we present a new technique for the estimation of short-term linear predictive parameters of speech and noise from noisy data and their subsequent use in waveform enhancement schemes. The method exploits a priori information about speech and noise spectral shapes stored in trained codebooks, parameterized as linear predictive coefficients. The method also uses information about noise statistics estimated from the noisy observation. Maximum-likelihood estimates of the speech and noise short-term predictor parameters are obtained by searching for the combination of codebook entries that optimizes the likelihood. The estimation involves the computation of the excitation variances of the speech and noise auto-regressive models on a frame-by-frame basis, using the a priori information and the noisy observation. The high computational complexity resulting from a full search of the joint speech and noise codebooks is avoided through an iterative optimization procedure. We introduce a classified noise codebook scheme that uses different noise codebooks for different noise types. Experimental results show that the use of a priori information and the calculation of the instantaneous speech and noise excitation variances on a frame-by-frame basis result in good performance in both stationary and nonstationary noise conditions.
Abstract-In this paper, we propose a Bayesian minimum mean squared error approach for the joint estimation of the short-term predictor parameters of speech and noise, from the noisy observation. We use trained codebooks of speech and noise linear predictive coefficients to model the a priori information required by the Bayesian scheme. In contrast to current Bayesian estimation approaches that consider the excitation variances as part of the a priori information, in the proposed method they are computed online for each short-time segment, based on the observation at hand. Consequently, the method performs well in nonstationary noise conditions. The resulting estimates of the speech and noise spectra can be used in a Wiener filter or any state-of-the-art speech enhancement system. We develop both memoryless (using information from the current frame alone) and memory-based (using information from the current and previous frames) estimators. Estimation of functions of the short-term predictor parameters is also addressed, in particular one that leads to the minimum mean squared error estimate of the clean speech signal. Experiments indicate that the scheme proposed in this paper performs significantly better than competing methods.
Solar occultation flux (SOF) measurements of alkenes have been conducted to identify and quantify the largest emission sources in the vicinity of Houston and in SE Texas during September 2006 as part of the TexAQS 2006 campaign. The measurements have been compared to emission inventories and have been conducted in parallel with airborne plume studies. The SOF measurements show that the hourly gas emissions from the large petrochemical and refining complexes in the Houston Ship Channel area and Mount Belvieu during September 2006 corresponded to 1250 ± 180 kg/h of ethene and 2140 ± 520 kg/h of propene, with an estimated uncertainty of about 35%. This can be compared to the 2006 emission inventory value for ethene and propene of 145 ± 4 and 181 ± 42 kg/h, respectively. On average, for all measurements during the campaign, the discrepancy factor is 10.2(+8,‐5) for ethene and 11.7(+7,‐4) for propene. The largest emission source was Mount Belvieu, NE of the Houston Ship Channel, with ethene and propene emissions corresponding to 440 ± 130 kg/h and 490 ± 190 kg/h, respectively. Large variability of propene was observed from several petrochemical industries, for which the largest reported emission sources are flares. The SOF alkene emissions agree within 50% with emissions derived from airborne measurements at three different sites. The airborne measurements also provide support to the SOF error budget.
Methane budgets (production = emissions + oxidation + recovery) were estimated for six landfill sites in Sweden. Methane oxidation was measured in downwind plumes with a stable isotope technique (Chanton, J. P., et al., Environ. Sci Technol. 1999, 33, 3755-3760.) Positions in plumes for isotope sampling as well as methane emissions were determined with an optical instrument (Fourier Transform InfraRed) in combination with N20 as tracer gas (Galle, B., et al., Environ. Sci Technol. 2001, 35, 21-25.) Two landfills had been closed for years prior to the measurements, while four were active. Measurements at comparable soil temperatures showed that the two closed landfills had a significantly higher fraction of oxidized methane (38-42% of emission) relative to the four active landfills (4.6-15% of emission). These results highlight the importance of installing and maintaining effective landfill covers and also indicate that substantial amounts of methane escape from active landfills. Based on these results we recommend that the IPCC default values for methane oxidation in managed landfills could be set to 10% for active sites and 20% for closed sites. Gas recovery was found to be highly variable at the different sites, with values from 14% up to 65% of total methane production. The variance can be attributed to different waste management practices.
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