Keywords: Biofuels, Energy crops, Greenhouse gas emissions, Land-use change, Life-cycle assessment 2

The greenhouse gas (GHG) balance of biofuels largely hinges on the magnitude of nitrous oxide (N 2 O) emissions from arable soils during feedstock production, which are highly variable. Here, used an agro-ecosystem model to generate these emissions at a high resolution over the Ile-de-France region in Northern France, for a range of feedstocks. The emissions were input to a life-cycle assessment of candidate biofuel pathways: bioethanol from wheat and sugar-beet, biodiesel from oilseed rape, and ethanol from miscanthus.

Compared to the widely-used methodology based on fixed emission factors, ecosystem modelling lead to 55% to 70% lower estimates for N 2 O emissions, emphasizing the importance of regional factors. The life-cycle GHG emissions of 1 st generation biofuels were 50% to 70% lower than fossile-based equivalents, and 85% lower for cellulosic ethanol.

Indirect land-use change effects negated these savings for bio-diesel and wheat ethanol, but were offset by direct effects for cellulosic ethanol.

Introduction

Controversy is still ongoing regarding the greenhouse gas (GHG) intensity of biofuels and bioenergy chains in general [START_REF] Kavanagh | Looking at biofuels and bioenergy -Commentary[END_REF][START_REF] Crutzen | N 2 O release from agro-biofuel production negates global warming reduction by replacing fossil fuels[END_REF]. While there appears a consensus on the benefits of displacing fossil fuels with energy from biomass, at least when excluding indirect land-use change (iLUC) effects [START_REF] Farrell | Ethanol can contribute to energy and environmental goals[END_REF][START_REF] Chum | Bioenergy[END_REF], the GHG savings reported in the literature may differ widely for seemingly similar pathways [START_REF] Chum | Bioenergy[END_REF]. These results are usually based on the life cycle assessment (LCA) methodology, relying on the same international standards but different calculation hypotheses and scope, in particular regarding co-product allocation methods, system boundaries, functional unit, or impact characterization. However, correcting for these differences to make these results commensurate only reduces part of their variability [START_REF] Farrell | Ethanol can contribute to energy and environmental goals[END_REF]. The source of input data used in the inventory step of the LCA plays a major role in its final outcome, in particular regarding crop management and biomass yields per unit area [START_REF] Davis | Management swing potential for bioenergy crops[END_REF]. Also, the emissions occurring upon the cultivation of energy crops in the field have a strong influence on the energy and GHG balances of the whole chain [START_REF] Smeets | Contribution of N 2 O to the greenhouse gas balance of first-generation biofuels[END_REF].

For instance, the efflux of nitrous oxide (N 2 O) resulting from fertilizer application accounted for 20 to 40% of the GHG emissions of the 1st generation biofuels consumed in France (ADEME, 2010), and depending on the methodology used this efflux could vary by a factor of 2, according to the same study. Worldwide, [START_REF] Smeets | Contribution of N 2 O to the greenhouse gas balance of first-generation biofuels[END_REF] showed that this uncertainty, when compounded with N 2 O emissions related to land-use changes could alter the life-cycle GHG emissions of biofuels by more than 50%, leading to higher emissions than their fossil counterparts in some cases.

In most LCAs of bioenergy chains, N 2 O emissions from soils are estimated with fixed emission factors (EFs), expressing a proportionality between N 2 O efflux and fertilizer N input rates [START_REF] Cherubini | GHG balances of bioenergy systems -Overview of key steps in the production chain and methodological concerns[END_REF]. However, these factors carry a large uncertainty. For instance on a global scale, the EF for direct emissions of N 2 O related to fertilizer N inputs to cropland varies between 0.3% and 3%, with a median value of 1% [START_REF] Eggleston | IPCC Guidelines for National Greenhouse Gas Inventories, prepared by the National Greenhouse Gas Inventories Programme[END_REF]. This worldwide default value were recently questioned based on the gap between bottom-up inventories of fertilizer-derived field emissions and the atmospheric build-up of N 2 O [START_REF] Crutzen | N 2 O release from agro-biofuel production negates global warming reduction by replacing fossil fuels[END_REF]. The higer EFs proposed by the latter authors would negate the GHG benefits of most bioenergy pathways, including 2nd generation biofuels from lignocellulose. It thus appears crucial to obtain more reliable assessments of the level of N 2 O emissions attributable to bioenergy feedstock production [START_REF] Cherubini | GHG balances of bioenergy systems -Overview of key steps in the production chain and methodological concerns[END_REF].

Emissions of N 2 O from soils are difficult to assess because they vary widely across time and space, depending on environmental conditions (soil properties and climate) and agronomic characteristics such as crop yields and management, and fertilizer use efficiency [START_REF] Stehfest | N 2 O and NO emission from agricultural vegetation: summarizing available of global annual emissions[END_REF]. Improving their estimation implies taking these drivers explicitly into account, and applying LCA at the supply area level rather than based on notional country-or European-wide 'average' cultivation fields [START_REF] Gabrielle | Life-cycle assessment of straw use in bio-ethanol production: a case-study based on deterministic modelling[END_REF]. This stresses the need for developing generic methods with a capacity to address the variability within these feedstock supply areas. Combining experimental monitoring of field-scale emissions with biophysical models appears as a promising avenue to provide reliable estimates of N 2 O emissions at a scale relevant for bioenergy units [START_REF] Dufossé | Using agroecosystem modelling to improve the estimates of N 2 O emissions in the life-cycle assessment of biofuels[END_REF]. However, there has been thus far very little work on the comparison of emission maps obtained with such bottomup estimates with top-down, integrative atmospheric measurements, such as tower fluxes.

Most top-down source inversion studies from atmospheric measurements have been carried out at large scales, from continental to global, with a coarse resolution of terrestrial sources of N 2 O [START_REF] Thompson | A Bayesian inversion estimate of N 2 O emissions for western and central Europe and the assessment of aggregation errors[END_REF].

The objectives of this paper were i/ to improve the estimations of field emissions of bioenergy feedstocks, in particular nitrous oxide, by using ecosystem modelling at regional scale, and ii/ to examine the impact of using such estimates in lieu of fixed emission factors in the LCA of current and prospective biofuel chains for the area. The modelling also made it possible to take into accounts the effects of land-use-change scenarios on the ouctome of the LCAs.

Here, we thus generated high-resolution maps of field emissions from in the Ile de France region (Northern France), using an agro-ecosystem model, for use in the LCA of representative biofuel chains for the area. The Ile de France region was selected because it was within the catchment probed by the atmospheric measurements, and is also included a set of field trials in Grignon (to the West of the domain) in which all the crops considered had been tested [START_REF] Gabrielle | Improved Assessment of the Greenhouse gas balance of bioenergy pathways (IMAGINE). Final project report[END_REF][START_REF] Goglio | 29% N 2 O emission reduction from a modelled low-greenhouse gas cropping system during 2009-2011[END_REF]. The maps of N 2 O emissions of were verified by using them as prior estimates in the inversion of the meso-scale atmosphericchemistry transport model CHIMERE, against concentrations recorded at various heights in the planetary boundary layer at 2 sites to the South of Ile de France, for the year 2007 [START_REF] Gabrielle | Improved Assessment of the Greenhouse gas balance of bioenergy pathways (IMAGINE). Final project report[END_REF]. The maps were subsequently expanded to test various feedstocks using a series of near-term future weather data to capture the effect of inter-annual variability and the long-term effects of perennial crops over their growing cycle (typically 20 years). The resulting fluxes were input into the life-cycle inventory of the following chains: biodiesel production from oilseed rape; bioethanol production from winter wheat and sugar-beet (firstgeneration biofuels); and bioethanol from Miscanthus x giganteus (a perennial C4 grass, further referred to as miscanthus), representing a prospective second-generation pathway.

The effect of feedstock production on soil C balances and dynamics were included in the LCA, relative to a reference land-use scenario in the absence of bioenergy production in Ile de France, thus account for direct LUC effects. Indirect LUC was taken into account based on the meta-analysis of De Cara et al. (2012).

Materials and Methods

Biofuel feedstocks and pathways

The biofuel value-chains considered included: bio-diesel from oilseed rape, ethanol from sugar-beet and winter wheat, and cellulosic ethanol obtained by enzymatic hydrolysis from miscanthus biomass. The baseline for the life-cycle inventory data was taken from a recent study carried out by the French Environment Agency on biofuels and their fossile equivalents in the context of France (ADEME, 2010). System boundaries included feedstock production to a regional storage after conversion in a biorefinery, but included CO 2 emissions upon combustion for fossile fuels.

The selected feedstock supply area was Ile de France, for which comparisons between ecosystem modelling and top-down atmospheric measurements were available to infer N 2 O fluxes. Simple assumptions were made for direct land-use changes (LUC) related to feedstock production. A recent study based on historical land-use data (In Numeri, 2012) concluded that in France 1 st generation biofuel crops mostly diverted existing crops from food to energy enduses, or displaced similar crops (eg, oilseed rape substituted pea crops). Only 13% of the cropland area used for biofuel production was taken on set-aside land, and this occurred mostly in regions outside of Ile de France. Therefore, direct LUC effects were neglected for arable crops. For the perennial lignocellulosic crop (miscanthus), the assumption was that the latter either displaced annual crops or was established on set-aside land. The associated direct LUC effects were simulated by considering the differences between the emissions occurring on the modelled miscanthus fields and those occurring in either the reference rotation of arable crops or a continuous fallow. Compared to rotational fallow, the latter represented 40% of the overall set-aside area in 2007, and were considered as a reference, conservative scenario for conversion of set-aside cropland to miscanthus. For lack of an adequate simulation model, continuous fallow was assumed to have the same C and N dynamics as the miscanthus fields.

Indirect land-use changes at the global scale (ie the cascading effects related to the diversion of food crops to fuel purposes) were accounted for in the GHG balance of biofuels based on the median values reported by De Cara et al. (2012) in their meta-analysis of the literature on this topic. The analysis accounts for biofuel type, feedstock type and production area, and we used the set of values that were the most relevant to the biofuel chains evaluated here.

Model simulations

Crop yields, soil C dynamics and emissions of reactive N (Nr), including N 2 O in particular were simulated with the agro-ecosystem model CERES-EGC [START_REF] Goglio | 29% N 2 O emission reduction from a modelled low-greenhouse gas cropping system during 2009-2011[END_REF] over the Ile de France region. Briefly, CERES-EGC was adapted from the CERES family of soil-crop models, with a focus on the simulation of environmental outputs such as nitrate leaching and gaseous emissions of ammonia and nitrogen oxides. It contains submodels for the major processes governing cycles of water, carbon and nitrogen in agro-ecosystem models. A physical module simulates the transfer of heat, water and nitrates down the soil profile as well as soil evaporation, plant water uptake, and transpiration in relation to climatic conditions. A microbiological module simulates the turnover of organic matter in the plough layer, involving both mineralization and immobilization of mineral N (denitrification and nitrification). CERES-EGC includes a submodel that simulates the production of NO and N 2 O through the nitrification and denitrification pathways. The model was adapted to miscanthus by explicitly introducing a rhizome pool and biomass translocation processes specific to perennial crops from one growing season to the next (Picoche, 2012).

Ile de France may be approximated as a 150 km x 150 km square area surrounding Paris, with 55 % cropland. A GIS database was constructed with available geo-referenced data on this region, including administrative borders, land-cover type, crop management practices, soil properties and climate. The corresponding layers of spatial information were mostly in vector format, and overlaid to delineate elementary spatial units representing unique combinations of soil types, weather data, and agricultural management. These units were subsequently used in the CERES-EGC simulations at the field-scale, in a bottom-up approach to map the emissions, using the procedure detailed by Dufosssé et al. (2013).

Since miscanthus is a perennial crop with a lifespan of up to 20 years, it was necessary to run long-term simulations, and also to capture inter-annual variability effects and soil organic C dynamics. We thus used weather data predicted for the 2010-2030 time slice by the DRIAS project in France [START_REF] Lémond | DRIAS -A step toward French Climate Services[END_REF], using the IPSL-CM4 model with the A1B GHG emission scenario from IPCC, which appeared as an intermediate scenario for air temperature and rainfall among the range of models and forcings tested by this project. Compared to the 1961-1990 period, air temperature would rise by 1.35 °C, with a average of 11.7 °C for the 2010-2031 time slice, and rainfall would remain level at an average of 641 mm yr -1 . CERES-EGC was run for the current land-use (2010) and the various feedstocks and land-uses hypothesized for biofuel production. For the verification year (2007), we used gridded weather data from the MCRU data base [START_REF] Chen | Constructing a consistent historical climate data set for the European domain[END_REF], with a 0.25° x 0.25° resolution, and cropland areas from the last available survey (pertaining to the year 2000).

An alternative, reference methodology for N 2 O emissions was based on the 2006 IPCC guidelines [START_REF] Eggleston | IPCC Guidelines for National Greenhouse Gas Inventories, prepared by the National Greenhouse Gas Inventories Programme[END_REF], as implemented in the baseline LCA (ADEME, 2010), using the Tier 1 approach (ie default emission factors). Indirect emissions of N 2 O due to leaching were calculated as 0.75 % of the nitrate losses, simulated either with CERES-EGC or expert knowledge (ADEME, 2010), while N 2 O emissions due to emissions of ammonia and nitric oxide were calculated as 1% of the latter fluxes, as simulated by CERES-EGC. In the ADEME methodology, nitrate leaching was given the same value for all crops except winter oilseed rape, for which specific field references were available. Regarding emissions related to the N content of crop residues, average values were derived from expert knowledge and field surveys, to which a 1% emission factor was applied, in accordance with [START_REF] Eggleston | IPCC Guidelines for National Greenhouse Gas Inventories, prepared by the National Greenhouse Gas Inventories Programme[END_REF].

3. Life-cycle inventories and impact characterization

Table 1 summarizes the sources of data used for the various steps of the biofuel chains. Crop management data were taken from surveys carried out by the French Ministry of Agriculture in 2006 for arable crops [START_REF] Gabrielle | Improved Assessment of the Greenhouse gas balance of bioenergy pathways (IMAGINE). Final project report[END_REF], and expert knowledge for miscanthus in Northern France [START_REF] Bessou | Greenhouse gas emissions of biofuels, Improving Life Cycle Assessments by taking into account local production factors[END_REF]; Table 2). The main agricultural inputs are listed in Table 3.

Miscanthus was considered unfertilized, but the N exports in harvested biomass were compensated for by adding a corresponding consumption of synthetic fertilizer N to the feedstock production system. Grain yields were obtained from regional simulations by CERES-EGC for arable crops, while miscanthus yields were calculated as 90% of the biomass simulated in late winter, correspond to a late harvest [START_REF] Strullu | Biomass production and nitrogen accumulation and remobilisation by Miscanthus giganteus as influenced by nitrogen stocks in belowground organs[END_REF]. The remaining 10% was returned to the soil as harvest residues.

Direct emissions of reactive N (Nr), whether gaseous or leaching, were simulated at regional scale with CERES-EGC over the 2010-2030 time period, along with C and water balances.

The predicted Nr losses included: nitrate leaching, emissions of ammonia, nitric oxide and nitrous oxide. Annual arable crops were included in the single following rotation, representative of current crop successions in the area: oilseed rape -winter wheat -sugarbeet -winter wheat -winter barley. For each crop in the rotation, daily simulated fluxes were accumulated from its sowing to the sowing of the following crop. Miscanthus was simulated as a single cycle from planting in 2010 to removal or re-establishment in 2030. Emission fluxes were averaged over the various cropping periods and expressed on a yearly basis for all crops. Fluxes were spatially averaged on the basis of the land area occupied by each crop or fallow, and a 5%-95% inter-quartile range was calculated from the distribution of emissions per ha in the relevant spatial simulation units, to derive a confidence interval in the LCA.

Data on biomass conversion to biofuels were taken from various sources relevant to France or Europe (Table 1). Feed co-products of 1 st generation biofuels were handled by system expansion using data from [START_REF] Lehuger | Environmental impact of the substitution of imported soybean meal with locally-produced rapeseed meal in dairy cow feed[END_REF] on soybean meal from Brazil, as a substitute for either rapeseed meal or dried distillers grains with solubles (DDGS) from wheat, and silage maize, as a substitute for sugar-beet pulp. Other co-products were dealt with by economic allocation [START_REF] Jungbluth | Life Cycle Inventories of Bioenergy Data v2.0[END_REF].

Rape methyl-ester (or bio-diesel) was produced from the esterification of rapeseed oil with fossile-based methanol, involving glycerin and potassium sulfate as co-products. Data for the conversion of sugar-beet to ethanol were taken from 2 plants located in the Champagne-Ardennes and Picardy regions about 200 kms to the North of Ile de France, considered representative of current best technologies [START_REF] Bessou | Greenhouse gas emissions of biofuels, Improving Life Cycle Assessments by taking into account local production factors[END_REF]. The conversion of wheat to ethanol involved dry-milling of wheat grains, saccharification and fermentation, and distillation to produce hydrated ethanol (95% basis). The production of the DDGS co-product from stillage included draff separation, concentration, drying and pelletization. The conversion of lignocellulose to ethanol involved steam pre-treatment of biomass, simultaneous saccharification and fermentation of the resulting mash, and distillation to hydrated ethanol. The vinasses produced by the fermentation process were digested to produce biogas and process heat and power. The data originally applied to a pilot plant using grass feedstock but we considered the latter could be replaced by miscanthus biomass due to the relative close chemical compositions of both feedstocks. In addition to ethanol, the process produced proteins and fibres and an economic allocation was made between the 3 end-products [START_REF] Jungbluth | Life Cycle Inventories of Bioenergy Data v2.0[END_REF].

Environmental impacts were characterized using the CML 2000 at mid-point level [START_REF] Guinée | Handbook of life-cycle assessment[END_REF]. The following impact categories were analyzed: eutrophication, acidification, photochemical ozone formation, global warming, and depletion of abiotic resources. The sensitivity of the global warming indicators to the spatial and temporal variations of N 2 O emissions in Ile de France was examined by calculating the GHG emissions of biofuels with the median and 5-95% inter-quartile range of these fluxes. LCA calculations were carried out with the Sima Pro software package (v7.1, Pré Consultants, Amersfoort, NL). observed yields differed by less than for 5% for winter wheat and oilseed rape, but CERES-EGC over-estimated the yields of sugar-beet by 22%. However, the sugar-beet yields averaged over the 2010-2030 simulations were closer to the values reported in 2007 (87 t fresh tuber weight (FW) ha -1 vs. 82 t FW ha -1 ), which mitigates the possibility of a model bias for sugar-beet. For comparison, sugar-beet yields ranged between 85 and 100 t FW ha -1 over the 2007-2012 period according to the regional agricultural statistics. For winter wheat, the 2010-2030 yields predicted by CERES-EGC were similar to the 2007 value (at 7.53 vs. 7.87 t grain dry matter (DM) ha -1 ), while they were 30% higher for oilseed rape. This increase for rapeseed is 3 times larger than the projections made by a set of agro-ecosystem models for 2030 in one site in Ile de France (Brisson and Levrault, 2011), while for wheat yields the latter predicted a 1 t DM ha -1 increase. These differences with our may due to the fact that no beneficial effects of rising CO 2 concentrations were included in the photosynthesis component of CERES-EGC for either crop, and to differences in the soil properties between our regional map and the particular site modelled by Brisson and Levrault (2011).

Results & Discussion

Field emissions of reactive N and soil C variations

According to CERES-EGC, the highest direct emissions of N 2 O occurred with sugar-beet (Table 2), due to its being a spring crop with fertilizer applications later into spring than the winter-sown crops (wheat and oilseed rape), when soil conditions, in particular its temperature are more conducive to denitrification. The high rate of N returned as crop residues by sugar-beet also explains these large fluxes. The emissions of miscanthus were 2- Indirect emissions of N 2 O due to leaching or gaseous losses of Nr, as well as the fate of crop residues, differed even more markedly between the 2 estimation methodologies (Table 4). The overall ranges across crops were larger with the ADEME than with the model-based methodology: 0.71 -2.80 kg N 2 O-N ha -1 yr -1 vs. 0.30-1.78 kg N 2 O-N ha -1 yr -1 , respectively.

Most of the variations arose from the contribution of crop residues, which was included in the direct emissions estimated with CERES-EGC (since the model simulates the fate of harvest residues), while this term reached up to 1.32 kg N 2 O-N ha -1 yr -1 for sugar-beet due to the large N content of the green leaves returned to soils at harvest. This was also captures in the model simulations, as mentioned above. Indirect emissions due to nitrate leaching were similar across the 2 approaches, with the exception of oilseed rape for which the model simulated extremely low nitrate losses.

As a consequence, the ranking of crops varied according to the N 2 O flux estimation methods: according to ADEME, annual crops had very similar emission rates, around 2.8 kg N 2 O-N ha -1 yr -1 , while miscanthus emitted 4 times less. Conversely, the modelled efflux from oilseed rape was close to that of miscanthus (less than 0.5 kg N 2 O-N ha -1 yr -1 ), while the other 2 annual crops emitted about 3 times more N 2 O, with sugar-beet in the upper end. The same pattern applied to nitrate leaching rates, with winter wheat resulting in the highest losses (at 37 kg NO 3 -N ha -1 yr -1 ). The leaching rate from wheat was close to the 40 kg NO 3 -N ha -1 yr -1 estimate given by ADEME (2010) as a flat rate for all annual crops except sugar-beet (with a value of 18 kg NO 3 -N ha -1 yr -1 derived from lysimeter studies). The low leaching rates (under 10 kg NO 3 -N ha -1 yr -1 ) simulated by CERES-EGC with winter oilseed rape arose from the capacity of this crop to take up N in fall. Similarly, the sowing of catch crops or the regeneration of volunteers, in particular before the sowing of the sugar-beet crop could have reduced the losses of nitrate overwinter several-fold (McDonald et al., 2005).

Gaseous emissions of NO were in the in the 0.4 -1.0 kg NO-N ha -1 yr -1 range for annual crops and twice lower for miscanthus (Table 3). Similarly to N 2 O, these values are under the 1-2 kg NO-N ha -1 yr -1 range reported by [START_REF] Stehfest | N 2 O and NO emission from agricultural vegetation: summarizing available of global annual emissions[END_REF] for cropland in Ile de France, using regionalized emission factors and a large data set of field observations. However, in previous tests against field data in the same area, the CERES-EGC model did not reveal any systematic bias in the prediction of NO emissions [START_REF] Rolland | Modeling of nitric oxide emissions from temperate agricultural ecosystems[END_REF]. Regarding ammonia (NH 3 ), the model simulated net emissions in the 2.3 -5.1 kg NH 3 -N ha -1 yr -1 range for annual crops, and a very small net deposition rate (-0.2 kg NH 3 -N ha -1 yr -1 ) for miscanthus. Overall, simulated gaseous losses of ammonia and nitric oxide were several-fold lower than the IPCC Tier 1 estimate, making up 10% of the fertilizer N inputs (for synthetic fertilizers). However, the modelled estimates are in line with the lower end of this emission factor, at 3% [START_REF] Eggleston | IPCC Guidelines for National Greenhouse Gas Inventories, prepared by the National Greenhouse Gas Inventories Programme[END_REF].

While there were no significant trends in terms of soil organic carbon (SOC) storage for annual crops, with rates in the -34 to +41 kg C ha -1 yr -1 range, miscanthus had a large capacity to sequester C, with rates around 575 kg C ha -1 yr -1 . The latter is in line with multiple longterm studies on miscanthus stands and the 680 kg C ha -1 yr -1 value reported in the review of [START_REF] Don | Landuse change to bioenergy production in Europe: implications for the greenhouse gas balance and soil carbon[END_REF] under stands more than 10 years old. This increase mainly reflected a buildup of soil organic matter due to large rates of litter inputs, combined with the protective effect of reduced tillage.

Life-cycle impacts of feedstock production

Figure 1 compares the impacts of the various candidate energy crops on a hectare (ha) and MJ of feedstock energy content basis. Direct land-use change effects were excluded for miscanthus in this analysis. The annual food crops (wheat, oilseed rape and sugar-beet) had the highest impacts per ha due to their high requirements in agricultural inputs compared to miscanthus, resulting in 60% to 85% lower impacts for the latter. Such pattern was noted by [START_REF] Monti | Cradle-to-farm gate life cycle assessment in perennial energy crops[END_REF] in Northern Italy, although to a lower extent, with 50% to 60% lower LCA impacts with miscanthus compared to a wheat-maize rotation on a ha basis.

The ranking among the 3 annual crops varied according to the indicator considered. The abiotic depletion (AD) and ozone formation potential (OFD) indicators were within a 10% relative range for all crops, while winter wheat and oilseed rape had 15% lower global warming potentials (GWP) compared to sugar-beet. Oilseed rape had 25% and 60% lower impacts than the other 2 annual crops due to its much low nitrate leaching rates in the field.

Overall, most of the differences in LCA impacts on a ha basis arose from the variations in Nr emission patterns across feedstocks.

Differences across crops were less pronounced when using one MJ of feedstock energy content (including grains and straw for annual crops) as a functional unit (Figure 1). The highest impacts generally occurred with winter wheat, except for the OFP which was slightly larger with winter wheat. Sugar-beet had overall lower impacts than oilseed rape and wheat, due its 40% higher energy yield per ha (Table 3). Similarly to the results per ha, the impacts of miscanthus per MJ were 80 to 98% lower than the annual crops, given that its energy output per ha was in the upper range of the crops. Overall, crops with a high energy output per ha (such as sugar beet and miscanthus) had comparably lower impacts per MJ than the others, showing that energy yields predominated in the trade-off between high productivity, fertilizer requirements and N 2 O emission rates. Miscanthus performed best because it minimzed this trade-off. The compensation of N exports by synthetic fertilizers accounted for 10 to 50% of the LCA impacts. Many miscanthus fields in France or the UK are currently unfertilized but is likely to lead to the depletion of soil nutrients in the long-run [START_REF] Strullu | Biomass production and nitrogen accumulation and remobilisation by Miscanthus giganteus as influenced by nitrogen stocks in belowground organs[END_REF]. Prior land use (cropland versus fallow) played a negligible role, bearing in mind that the effects on soil C dynamics were not included at this stage and thus the differences between the two scenarios only reflected variations in terms of soil distribution over Ile de France.

Life-cycle impacts of biofuel chains

Figure 2 reports the GHG emissions of the various biofuel chains investigated here, compared to their fossile equivalents. When excluding land-use change effects (ie on an attributionnal basis), all biofuel chains emitted less GHG than the fossile fuels, with savings ranging from 50% (ethanol from winter wheat) to 80% (cellulosic ethanol). For the 1 st generation biofuels, the figures based on modelled field emissions (referred to as CERES) were slightly lower than those based on default emission factors (ADEME). The lower field N 2 O emission rates with the former methodology were in a large part compensated for by the differences in co-product handling: system expansion with CERES vs. energy-based allocations for ADEME. The latter generally results in a lower environmental burden on biofuels than the former [START_REF] Cherubini | GHG balances of bioenergy systems -Overview of key steps in the production chain and methodological concerns[END_REF]. The GHG emissions of miscanthus-based ethanol were only 60% lower than those of sugar-beet ethanol (the 1 st generation chain with the lowest emissions), and 75% lower than the other biofuels. This gap is larger than with the feedstock data (Figure 1) because of the relatively high biomass-to-ethanol yield hypothesized in the conversion process due to the economic allocation with the other end-products. This yield is around 0.60 kg ethanol kg -1 feedstock DM, higher than the theoretical yield and the 0.24 value proposed by a recent blueprint design for corn stover [START_REF] Humbird | Process Design and Economics for Biochemical Conversion of Lignocellulosic Biomass to Ethanol. Dilute-Acid Pretreatment and Enzymatic Hydrolysis of Corn Stover[END_REF]. Thus, changing the allocation system and process design with fewer co-products would probably have resulted in larger impacts for miscanthus ethanol.

The GHG balances presented here fell in the middle or lower end of the ranges given by [START_REF] Chum | Bioenergy[END_REF] worldwide: 22-44 g CO 2 eq./MJ for sugar-beet ethanol, 16-70 eq./MJ for wheat ethanol, 5-75 g CO 2 eq./MJ for bio-diesel from plant oils, and -7 to 47 g CO 2 eq./MJ for cellulosic ethanol. Aside from the low Nr emission rates specific to the CERES methodology, this pattern is probably due to the use of recent technologies for conversion (representative of the last decade), and biomass yields higher than world averages for arable crops.

Accounting for direct LUC effects for miscanthus grown on cropland offset most of the lifecycle emissions due to the high capacity of this perennial grass to sequester C with respect to arable crops (Table 2), resulting in a net value close to the bottom end of the range from [START_REF] Chum | Bioenergy[END_REF]. The inclusion of ecosystem C pools in LCA and in particular the effects of land-use and management practices on soil organic C dynamics has been shown to exert a large influence on the GHG abatements of bioenergy. A case-study for miscanthus in the UK concluded to a potential for negative emissions under -40 g CO 2 eq. MJ -1 [START_REF] Davis | Management swing potential for bioenergy crops[END_REF].

Including indirect LUC effects dramatically altered the GHG savings of all biofuels, including cellulosic ethanol from feedstock grown on cropland (Figure 2 The uncertainty on N 2 O emissions due to their temporal and spatial variability in Ile de France resulted in asymetric confidence intervals (5%-95%) for the net GHG balance of biofuels, with a magnitude ranging from 2 to 25 g CO 2 eq./MJ. This value was largest with wheat ethanol and bio-diesel. The fact that the confidence intervals of these 2 biofuels overlapped indicate that their net GHG emissions were not significantly different. Conversely, the differences between cellulosic ethanol, sugar-beet ethanol and the other 2 biofuels were significant (p>0.9). In their uncertainty analyses on the influence of N 2 O emissions on the GHG balances of biofuels, [START_REF] Smeets | Contribution of N 2 O to the greenhouse gas balance of first-generation biofuels[END_REF] reported much larger confidence intervals, similar to life-cycle totals for ethanol from wheat or bio-diesel from oilseed rape. This arose from the large uncertainties on field emissions (ranging from 1 to 4 kg kg N 2 O-N ha -1 yr -1 for winter wheat in Europe), and emphasizes the benefits of regional modelling to reduce the magnitude of this uncertainty.

Figure 3 shows the LCA results of biofuel chains for a broader range of impact categories. As could be expected from the feedstock comparison, miscanthus ethanol had the lowest impacts, whether established on cropland or fallow. Compared to gasoline, cellulosic ethanol had an 8fold lower acidification potential but a 5-fold larger impact for eutrophication (ADEME, 2010). The LCA results based on ecosystem modelling (CERES) were generally lower than those obtained with the fixed emission factors (ADEME), by a factor of up to ten. These gaps were much larger than for the GHG emissions because feedstock production was the top contributor to these impacts, and the relative differences between ADEME and CERES were in a similar range for Nr emissions (Table 3). Differences between 1 st generation biofuels were more pronounced with the ADEME methodology.

Overall, sugar-beet ethanol performed better than wheat ethanol and bio-diesel, except for eutrophication for which bio-diesel and miscanthus ethanol were on a par according to the CERES estimation, due to similar nitrate leaching rates. Only a few published LCAs of biofuels include impacts other than GHG emissions or fossile energy consumption. They generally conclude to the same pattern as evidenced here, with short-range impacts such as eutrophication, ozone formation or acidification potentials being larger with biofuels than their fossile counterparts due to Nr emissions upon feedstock cultivation [START_REF] Gabrielle | Life-cycle assessment of straw use in bio-ethanol production: a case-study based on deterministic modelling[END_REF]. However, in the case of lignocellulosic plants such as miscanthus, cobenefits in terms of control soil erosion or nutrients filtering have also been pointed out [START_REF] Chum | Bioenergy[END_REF]. At the field-and landscape scales, management practices such as cover crops and buffer zones may thus be introduced to mitigate these local impacts and optimize biofuel chains [START_REF] Davis | Management swing potential for bioenergy crops[END_REF][START_REF] Bessou | Greenhouse gas emissions of biofuels, Improving Life Cycle Assessments by taking into account local production factors[END_REF]. These options may be investigated using the regional modelling systems presented here at the plot-scale, and with the use of integrated landscape models [START_REF] Dufossé | Using agroecosystem modelling to improve the estimates of N 2 O emissions in the life-cycle assessment of biofuels[END_REF].

Overall, sugar-beet ethanol thus emerged as the 1st generation biofuel with the best performance due to its high energy output per unit area, and miscanthus ethanol was still more efficient at abating GHG emissions while minimizing local impacts. Establishing miscanthus on fallow land proved the best option, but is limited in practice by the availability of fallow land in Ile de France (which only makes up 4% of the utilizable arable area in the region). It should also be borne in mind that the data on miscanthus conversion to ethanol is based on an extrapolation of laboratory-scale processes, thus still fraught with a large uncertainty and more prospective in nature. Its results should therefore be considered as indicative rather than representative of a commercial plant. Results from currently-operating pilots should help improve life-cycle inventory data for this process in a near future.

Conclusion

We assessed the environmental impacts of several biofuel feedstocks and pathways at the regional scale, based on either annual or perennial crops. The use of an ecosystem model to estimate for the emissions of reactive N, as opposed to fixed emission factors, lead to a 5% to 15% reduction in the overall GHG emissions of biofuels, due to local soil and climatic characteristics. The GHG savings incurred by the substitution of fossile fuels with biofuels hinged on land-use change scenarios and biofuel pathways, being highest for cellulosic ethanol and lowest for bio-diesel from oilseed rape.

Table 3

 3 lists the field emissions of reactive N (Nr) and soil organic C (SOC) variations for the various feedstocks obtained with the ecosystem model CERES-EGC. Crop yields predicted in 2007, the reference year for the verification of N 2 O fluxes (Gabrielle et al., 2012) are compared to statistics reported by the Ministry of Agriculture for the same year. Modelled and

to 5 -

 5 fold lower than those of the annual crops, in agreement with Drewer et al. (2012), while winter crops had intermediate emission levels. Except for sugar-beet, the direct emissions of N 2 O simulated by CERES-EGC were 2-to 4-fold lower than output by the ADEME methodology. Such pattern was already noted in the verification of 2007 over France, for which the atmospheric inversion suggested emission factors (EF) half lower than the default 1% value of the IPCC guidelines for cropland (Gabrielle et al., 2012). Deviations between modelled and IPCC Tier 1 estimates for N 2 O have been reported in both directions, depending on local climatic and management conditions. An EF of 0.80% was derived for cropland in China with the DNDC model (Li et al., 2001), while Leip et al. (2011) reported values similar to the IPCC Tier 1 range in Europe, with values around the 1% median in France over the 1990-2000 time slice. Here, the annual amounts and seasonal distribution of rainfall were similar between the current and near-future climates in Ile de France, which should not have altered the denitrification patterns. Therefore, the low emission levels simulated over 2010-2030 in Ile de France compared to 2007 or other estimates were probably not due to changes in climate forcing.

  ). It resulted in negative values for bio-diesel and wheat ethanol, and divided the savings by 2 for sugar-beet and miscanthusbased ethanol grown on cropland, compared to attributional values. Such patterns are common in the literature on consequential LCA, whether based on macro-economic sectoral modelling or simpler substitution and displacement schemes (De Cara et al., 2012; Silalertruksa et al., 2009). For cassava-based ethanol in Thailand, the latter authors cited LUC-related GHG emissions in the 20% to 90% range compared to attributional values, depending on the crops displaced and crop production regions.

Figure 1 .

 1 Figure 1. Compared cradle-to-farm-gate LCA results for the various feedstocks per ha (top) and MJ of biofuel feedstock energy content (bottom). Two scenarios were implemented for miscanthus : conversion from cropland or fallow land. Direct effects of land-use changes are excluded here.

Figure 2 .

 2 Figure 2. Life-cycle emissions of greenhouse gases per MJ of of biofuels and their fossile equivalents. EtOH : ethanol, RME: rape methyl-ester; W: wheat; SB: Sugar-Beet. ADEME refers to the ADEME 2010 study and methodology, and CERES to the modelled emissions of Nr. Emissions related to direct and indirect land-use changes are noted dLUC and iLUC, respectively. The error bars correspond to the 5%-95% confidence intervals with respect to the field emissions of N2O.

Figure 3 .

 3 Figure 3. Relative life-cycle impacts of biofuels in Ile de France using either regional ecosystem modelling (CERES) or IPCC Tier 1 emission factors (ADEME), per MJ of biofuel energy content. EtOH : ethanol, RME: rape methyl-ester; SB: Sugar-Beet; Misc: miscanthus.

Figure 1 .Figure 2 .

 12 Figure 1. Compared cradle-to-farm-gate LCA results for the various feedstocks per ha (top) and MJ of biofuel feedstock energy content (bottom). Two scenarios were implemented for miscanthus : conversion from cropland or fallow land. Direct effects of land-use changes are excluded here.

Figure 3 .

 3 Figure 3. Relative life-cycle impacts of biofuels in Ile de France using either regional ecosystem modelling (CERES) or IPCC Tier 1 emission factors (ADEME), per MJ of biofuel energy content. EtOH : ethanol, RME: rape methyl-ester; SB: Sugar-Beet; Misc: miscanthus.

Table 1 .

 1 Sources of data for the life-cycle inventory of the pathways investigated. AGRESTE refers to the Statistical Bureau of the French Ministry of Agriculture. References: 1:Bessou, 2009; 2: Gabrielle et al., 2012; 3: Jungbluth et al., 2007 ; 4: ADEME, 2010 : residues left after removal of the miscanthus stand (below-ground N stock estimated at 100 kg N ha -1 in late spring ; Strullu et al., 2011).

		RME CERES	RME ADEME	Wheat EtOH CERES	Wheat EtOH ADEME
		SB EtOH CERES	SB EtOH ADEME	Misc EtOH / cropland	Misc EtOH / fallow
		120
		100
		80
	%	60
		40
		20
		0
		Abiotic depletion	Acidification	Eutrophication	Ozone formation

c

Table 4 .

 4 Estimates of N 2 O emissions (kg N 2 O-N ha -1 yr -1 ) for the various feedstocks obtained with the agro-ecosystem model CERES-EGC or the IPCC 2006 guidelines (ADEME, 2010).

Acknowledgements

These results were obtained in the framework of the IMAGINE project funded by the ENERBIO programme of the TUCK foundation (Rueil-Malmaison, France).


Ali Khanagha
email: khanaghaa@ripi.ir

Vahid Khanagha
email: vkhanagha@ee.iust.ac.ir

Selective Tap Training of FIR filters for Blind Source Separation of Convolutive Speech Mixtures

This Paper presents a novel low complexity time domain algorithm for blind separation of speech signal from their convolutive mixtures. We try to reduce intrinsic computational complexity of time domain algorithms by adapting only a small subset of taps from separating FIR filters which are expected to attain largest values. This selection is accomplished by recovering spatial dependencies using Linear Prediction (LP) analysis. Then we use Particle Swarm Optimization (PSO) in order to find best values for these selected taps. We employ the sparseness properties of speech signals in the Time-Frequency (TF) domain to define a low complexity and yet appropriate fitness function which numerically quantifies the amount of achieved separation by each one of the particles during PSO execution.

I. INTRODUCTION

Blind Source Separation (BSS) has widely received attentions since it was the common problem for almost every multi sensory application. BSS consists in separation of N unknown, yet statistically independent sources, ���� � �� � ������� N ���� T , from M observed mixtures of these sources, ���� � �� � ������� M ���� T , where the mixture parameters are unknown. This would be a challenging task, especially in real world scenario when each sensor receives multiple copies of each source due to the presence of reverberation phenomena. This is often the case for speech signals in typical echoic acoustic environments. The relation between inputs and outputs of such convolutive mixing system can be written as

� � ��� �� � � �� ������ � ��� ��� � ���� � ���
Where � �� ��� is the fir impulse response between jth source to the ith sensor. Several methods have been proposed for solving this problem. A comprehensive review of these techniques can be found in [START_REF] Syskind Pedersen | A survey of convolutive blind source separation methods[END_REF]. Here we consider time domain BSS algorithms in which, Independent Component Analysis (ICA) is directly applied to mixtures and unmixing FIR filters are directly estimated. They have the advantage of possible convergence near the optimal point and inversion of mixing system but they suffer from computational complexity of adaptation of very long FIR filters [START_REF] Buchner | A generalization of blind source separation algorithms for convolutive mixtures based on second-order statistics[END_REF]. Also time domain methods are prone to the so called whitening effect; since most of these algorithms were developed for i.i.d sources, they tend to reduce temporal dependencies as well as spatial dependencies [START_REF] Kokkinakis | Multichannel blind deconvolution for source separation in convolutive mixtures of speech[END_REF]. This might be a problem for speech signals with their special temporal structure.

In this paper, we present a novel time domain algorithm which proceeds in two steps. First, we identify potentially important taps of unmixing FIR filters by identifying spatial dependencies using Linear Prediction (LP) analysis. Then we use Particle Swarm Optimization (PSO) technique in order to find best values for these selected taps. PSO is a stochastic, population-based evolutionary algorithm for problem solving in complex multidimensional parameter spaces. The main challenge of PSO is to find an appropriate fitness function with low computational complexity and yet with a global extremum at the true separation point. We define a novel cost function based on TF domain sparseness properties of speech signals. Keeping these equations in mind, along with the fact that mixing FIR filters are the causes for spatial dependencies between observed mixtures, brings the possibility of attaining 978-1-4244-4683-4/09/$25.00 ©2009 IEEE a general knowledge about unmixing system by studying these spatial dependencies.

II. IDENTIFICATION OF SPATIAL DEPENDENCIES

For example, if we observe that one mixture has a strong dependency with p lag delayed version of the other mixture, we may expect that corresponding mixing FIR filter, had a large value on its pth tap. For now, we leave the discussion for finding the value of this tap for section III and focus on how to extract the structure of these dependencies from speech mixtures.

Because of the temporal structure of speech signals, in which every sample of each speech signal has strong dependencies with its adjacent samples, it is useless to observe simple cross correlation between mixtures. For example, Fig. 2, shows the resulting covariance matrix for a very simple anechoic 1 mixture, calculated from raw observations. This covariance matrix has a special sylvester structure which corresponds with a special sylvester structure used in [START_REF] Buchner | A generalization of blind source separation algorithms for convolutive mixtures based on second-order statistics[END_REF]. There exists four distinctive blocks corresponding to each one of four FIR filters of mixing system. In each block, parallel slant lines are representatives for taps of FIR filters (lower triangular section of each block must be studied for each filter). In Fig. 2, Although the spatial dependencies are observable as two thick slant lines on non diagonal blocks, but the exact position of this dependency is non distinctive. Specially, in more realistic mixing scenarios, where several reflections of sources to each sensor result in several slant lines, they are likely to overlap with each other and make this problem even harder.

Instead, we use LP analysis as a pre processing step to dismiss these temporal dependencies. We can generate a whitened version of mixtures using LP residuals; however, in order to preserve spatial dependencies, this prewhitening must be done in blocks of smaller length than 5 milliseconds [START_REF] Das | Synergy of Particle Swarm Optimization with Evolutionary Algorithms for Intelligent Search and Optimization[END_REF]. Using these short blocks of prewhitened mixtures, we use the exact formulation of [START_REF] Buchner | A generalization of blind source separation algorithms for convolutive mixtures based on second-order statistics[END_REF] to form the covariance matrix, which is now expected to solely represent spatial dependencies. Fig. 3, presents the resulting covariance matrix of prewhitened version of the same mixture. After applying some thresholding and 2D median filtering (in order to retrieve broken lines and suppress single meaningless points), the location of remaining slant lines gives us the exact location of strongest taps of mixing FIR filters.

Obviously, the real convolutive mixtures have larger orders of spatial dependencies, because they are formed by more complex FIR filters; However we can always select a small subset of strongest dependencies (strongest slant lines). Also we accept possible false detections, since we will run an optimization procedure, as will be explained in section III, which is able to find the best possible values for selected taps; in fact, we will train these selected taps to produce outputs that are as different (independent) as possible. In this way, the few falsely identified taps, would attain very small values near the separation point. 1 each sensor receives only one copy of sources with different time delays.

III. SELECTIVE TAP TRAINING PROCEDURE

We use PSO as our optimization technique in order to find the most fitted values for selected taps of FIR filters.

A. PSO principles

Particle swarm optimization is a stochastic, populationbased evolutionary algorithm for problem solving in complex multidimensional parameter spaces. It is a kind of swarm intelligence that is based on social-psychological principles.

A multidimensional optimization problem is given, along with an objective function to evaluate the fitness of each candidate point in parameter space; The swarm is typically modeled by particles in this multidimensional space that have a position and a velocity. After the definition of a random population of individuals (particles) as candidate solutions, they fly through hyperspace of parameters with the aid of two essential reasoning capabilities: their memory of their own best local position and knowledge of the global or their neighborhood's best [START_REF] Krusienski | The Application of Particle Swarm Optimization To Adaptive IIR Phase Equalizer[END_REF]. PSO begins by initializing a random swarm of � � particles � � � �� �� ���� �� �, each having � parameters. At each iteration, the fitness of each particle is evaluated according to selected fitness function. The most fit experienced position of each particle is stored and progressively replaced as ����� � �� � ����� � along with a single most globally fit particle (������ as fitter locations are encountered during algorithm iterations. The parameters of each particle in the swarm are updated at each iteration (n) according to a velocity vector as [START_REF] Das | Synergy of Particle Swarm Optimization with Evolutionary Algorithms for Intelligent Search and Optimization[END_REF]:

��� � ����� ��� � � � ��� � ����� ��������������� ���� ���� � � ������ � �� � ���� � � ��������� � ������ ���� � � ������ � �� � ���� � � ������� � �� � ������ � � ��� �� � ����� � ��� � ��� ���
Here, ��� � ����� ��� is the velocity vector of particle i, � � � ����� is a random value, ��� � ������� � are acceleration coefficients toward ����� and ����� � and � is the inertia weight.

In fact, the trajectory of each particle is determined by random superposition of its previous velocity with the location of local and global best particles found by far. As new gbests are encountered during the update process, all other particles begin to swarm toward this new ����� , continuing their random search along the way. The optimization is terminated when all of the particles have converged to ����� or a sufficient condition of fitness function met.

The advantages of this optimization procedure for our case is its independence from the structure of underlying process. It doesn't need fine parameter selections and it doesn't require to consider source statistics in its update equations (in the form of probability distribution function (pdf) estimations).

B. Implementation of PSO for selective tap training

The most challenging task in implementing PSO for any optimization problem is to define a well behaved objective function which has a global extremum at the desired point. However, the computational complexity of such an objective function must be low enough since it must be calculated for large population of particles in every iteration of algorithm.

In our case, we need to define a cost function which quantifies the amount of achieved independence between two output signals, with current tap values. This is a challenging task for a wideband signal such as speech. It is apparent, that cross correlation metrics might result in self decorrelation instead of spatial decorrelation. Other measures of independence, such as those based on higher order statistics are too complicated to calculate for all of the particles. Also they are mostly developed for i.i.d sources and again they might result in temporal decorrelation. Here we define a novel, low complexity fitness function, which effectively measures the amount of achieved independence.

Our fitness function is based on sparsness properties of speech signals in Time-Frequency (TF) domain. A signal is said to be sparse in a given basis, when it is zero or nearly zero more than might be expected from its variance [START_REF] O'grady | Survey of sparse and non-sparse methods in source separation[END_REF]. Speech signals are known to have such sparse representation in TF domain. This sparsness of speech signals, which is caused by different system properties of speakers and different sentences in their speech, causes the TF domain distribution of two different speech signals to be mostly disjoint (i.e. there is only one active source in most of given TF points) [START_REF] O'grady | Survey of sparse and non-sparse methods in source separation[END_REF]. Hence, it seems reasonable to define a cost function that measures the amount of disjointness in TF domain and trying to maximize it. Fig. 3, demonstrates the superimposed TF distribution of two pure speech signals, after applying an appropriate threshold. There, white color corresponds to TF overlaps and black and gray colors represent the points where only one source is active. It can be seen clearly that two sources rarely overlap with each other. In contrast, Fig. 4 shows the TF distributions of two observed measured mixtures, which almost completely overlap with each other. Clearly, the maximization of TF domain disjointness is equal to minimization of the overlap between high power TF points of two outputs. But we must be aware of the possible frequency selectivity of separating filters. In fact, when we apply such measure to the whole spectrogram, it is very possible that we achieve two low pass and high pass signals (i.e. the TF distribution of outputs are non overlapping, but only in frequency domain). Although our incompletely defined fitness function is minimized and sources are disjoint in TF domain but they are not representatives of two separated sources. We cope this possible divergence of our fitness function by partitioning the spectrogram to several smaller windows and evaluating the fitness function in these smaller windows with the condition of the presence of both outputs in each window. In other word, if the amount of energy in each window is higher than a threshold for both outputs, we may calculate fitness of that window and sum it up with total fitness function. So we are sure that if we met disjointness in a given window, since both outputs were present there, no frequency selectivity has been occurred.

In this way, at the beginning, since the outputs are not separated yet, there exist many valid windows with large amount of TF overlap. Later, as the optimization process proceeds, the amount of overlaps are decreased and probably the number of valid windows degrades and the total fitness function becomes less and less. The selected window must be able to cover at least a vowel, since it must cover the simultaneous activities of two different speakers. So, it is reasonable to choose a time duration of 0.3 seconds for this window. The frequency width of window is chosen to be around 40-50 Hz; wide enough to cover at least one harmonic activity from each active source and narrow enough in order to avoid possible frequency selectivity inside the window (the frequency resolution of separating filters must be considered, which is directly proportional with the length of the FIR filter).

Finally, it should be noted that since we selected a small subset of taps of FIR filters for training, we have already abandoned many possible divergences at the first place. We have selected the taps that are likely to be most effective taps in separation task. It's the key feature of our proposed algorithm which guarantees fast convergence of particles of PSO around separation point. If we choose to train all of the taps of FIR filters, then our fitness function wouldn't be able to optimize them because of the strong possibility of divergence around temporal whitening point.

IV. SIMULATION RESULTS

In this section we present simulation results for proposed algorithm. First, we study the results for the proposed BSS method and then we compare the feasibility of using each feature set for resolving permutation ambiguity, on a large database of different speech sources.

we compare performance of the proposed BSS method with that of [START_REF] Das | Synergy of Particle Swarm Optimization with Evolutionary Algorithms for Intelligent Search and Optimization[END_REF]. A ��� transfer function H��� was chosen as � ����� ��� ���� ��� ���� ��� ��� ��� ���� ��� ���� ��� ���� ��� ��� ��� � ���� ��� ���� ��� ����� ��� ���� ��� ���� ��� �

The measure of performance will be Signal-to-Interference-Ration (SIR) which is the ratio between the energy of desired signal and the cross-talk between channels, i.e.,

��� � �� ��� � ��������������� � �������������������� � � ���

Since the proposed method is based on the TF domain distribution of sources, which is strongly dependent to the speaker and the uttered speech, it is necessary to evaluate the suitability of our fitness function for a large number of speech signals. Therefore, we have used a database of 30 different speech signals (15 male speakers versus 15 female speakers) of length 3 seconds, to form 200 different combinations of sources. We use a population of 100 particles and choose 4 taps from each FIR filter for training. In other word, we have 12 parameters to be optimized during the PSO. The maximum number of iterations is set to 50 and the order of LP analysis filters is 15.

Table I, summarizes the average SIR results for this 200 different combinations of sources which demonstrates the slight improvement in separation quality compared to [START_REF] Das | Synergy of Particle Swarm Optimization with Evolutionary Algorithms for Intelligent Search and Optimization[END_REF]. Also, another experiment is done in order to test the feasibility of proposed method for identifying spatial dependencies. Thus, 50 different impulse responses, which are more complex than the one used in previous experiment are employed and the number of false detections in identifying the three most strongest taps of each of 4 sub channels are counted. From this �� � �� � ��� detections, only 67 false detections are observed, which proves the fidelity of proposed method in 88% of detections.

Beside the reduced computational complexity, the most important feature of the proposed method is its ability to train the whole four FIR filters shown in Fig. 1. Most of the gradient based time domain algorithms for speech signals, like the one in [START_REF] Das | Synergy of Particle Swarm Optimization with Evolutionary Algorithms for Intelligent Search and Optimization[END_REF], are not able to train � �� and � �� because of the structure of their update equation. The cost function used in [START_REF] Das | Synergy of Particle Swarm Optimization with Evolutionary Algorithms for Intelligent Search and Optimization[END_REF] is based on block diagonalization of Correlation matrix and concentrates on the cancellation of mutual spatial dependencies between two outputs. This method is incapable of reducing self dependencies of each output.

Instead, our proposed method is able to train all four FIR filters with its evolutional intelligence and based on equations (1) and (2), exhibits very good performance in channel identification. We use normalized mean squared error (NMSE) of channel identification as another performance index as:

���� � � � �� ���� �� �� �� � ��� �� �� ���� � � ��� � � ��� � �� �� ���� � � ��� � ��� � ��� � ���
Table II, summarizes the NMSE for 200 different combinations of sources which demonstrates significant improvement in identification quality compared to [START_REF] Das | Synergy of Particle Swarm Optimization with Evolutionary Algorithms for Intelligent Search and Optimization[END_REF]. Also, with the selection of this small subset of taps, which are truly responsible for spatial dependencies, we can successfully avoid the possible temporal whitening effect of regular time domain algorithms.The other strong aspect of the proposed method is that it is needless for fine parameter justifications (such as adaptive step parameter of update equation in [START_REF] Das | Synergy of Particle Swarm Optimization with Evolutionary Algorithms for Intelligent Search and Optimization[END_REF]) or identification of statistical properties of sources (like the pdf estimation in information theoretic based methods).

V. CONCLUSION

We Proposed a novel method for the BSS of convolutive mixtures which is based on extracting spatial dependencies and direct concentration on removing them. We used PSO as optimization process, which doesn't require fine parameter justifications and pdf estimations. The most challenging aspect of implementing PSO was to define an appropriate cost function for its evaluations which was accomplished using TF domain sparsity properties of speech signals. Simulation results demonstrated the slight improvement in separation quality. Beside the reduced computational complexity, the ability to train all of the 4 unmixing FIR filters simultaneously and its reliability against temporal whitening effect was the main promises of the proposed method. It also demonstrates about 5 dB improvement in NMSE of channel identification.

Fig. 1 .Fig. 1 .

 11 Fig. 1. Block diagram of mixing and unmixing systems.

Fig. 2 .

 2 Fig. 2. Covariance matrix computed from raw mixtures of an anechoic mixture. The exact location of thick slant lines aren't distinctive enough to carry information about maximum valued taps of mixing system.

Fig. 2 .

 2 Fig. 2. Covariance matrix of prewhitened mixtures of an anechoic mixture.The exact locations of strong spatial dependencies are easy to extract.

Fig. 3 .

 3 Fig. 3. TF distribution of two pure speech signals. The two sources are almost completely disjoint.

Fig. 4 .

 4 Fig. 4. TF distribution of two observed mixtures of speech. Two mixtures are almost completely non disjoint.

TABLE III AVERAGE

 III NMSE IMPROVEMENT FOR 200 DIFFERENT COMBINATIONS OF SPEECH SIGNALS.

	Algorithm	NMSE 1	NMSE 2	Average
	Selective training	-5	-5.45	-5.2
	SOS [2]	-0.2	-0.3	-0.25

IEEE Symposium on Industrial Electronics and Applications (ISIEA 2009), October 4-6, 2009, Kuala Lumpur, Malaysia


Vahid Khanagha
email: vkhanagha@ee.iust.ac.ir

Ali Khanagha
email: khanaghaa@ripi.ir

Solving global permutation ambiguity of time domain BSS using speaker specific features of speech signals

Multidimensional localization of multiple sources using BSS based TDOA estimators, requires the solution of global permutation ambiguity before fusing several TDOA estimations. Since the separation quality of BSS isn't always perfect, it is not easy to decide which TDOA belongs to which source. Here we study the possibility of using several speaker specific features of speech signal in order to recognize perceptually dominant sources in each one of moderately separated outputs of BSS algorithm. We compare the feasibility of different features in terms of validity rate of decisions and computational complexity.

I. INTRODUCTION

Fusing several TDOA estimates from multiple microphone arrays is a well known method for multidimensional localization of speech sources. Since traditional methods such as generalized cross correlation (GCC) are incapable of estimating TDOAs of multiple active sources in reverberant environments, the use of time domain BSS in TDOA estimation has introduced [START_REF] Buchner | Simultaneous localization of multiple sound sources using blind adaptive MIMO filtering[END_REF]. The BSS based TDOA estimation, not only provides robust TDOA estimates for multiple sources, but also separates original signals from the observed mixtures which can be used as a clue for the fusion of estimated TDOAs in order to accomplish spatial localization of active sources.

In fact, even if traditional methods, like GCC, were capable of precise TDOA estimation of multiple simultaneous sources, they still couldn't decide which TDOA from each array corresponds to which TDOA from other arrays. Although Some heuristic methods have been reported, like the outlier elimination in [START_REF] Jan | Sound Source Localization in Reverberant Environments using an Outlier Elimination Algorithm[END_REF] and Inter-aural Time and level difference based method in [START_REF] Shimoyama | Multiple acoustic source localization using ambiguous phase differences under reverberant conditions[END_REF], but they often suffer from algorithm complexity and the lack of generality. on the other hand, the use of time domain BSS for TDOA estimation provides the separated sources as a by-product which is ultimately valuable in relating different TDOAs from several arrays to each other and solving the global permutation ambiguity. With perfect separation quality, we can relate outputs of different BSS algorithms executed on different arrays with simple cross correlations as reported in [START_REF] Lombard | Multidimensional Localization of Multiple Sound Sources Using Blind Adaptive MIMO System Identification[END_REF]. But, usually speech separation quality is not perfect and we often achieve just a moderate amount of gain in Signal to Interference Ratio (SIR). In practice, for a mixture situation, both sources would be present on both outputs but only one of them would be perceptually dominant (to some extent). But this dominancy is not enough to let us use cross correlations for solving permutation ambiguity.

Here, we assume the mixture scenario and propose to use speaker specific features to qualify this relative dominancy for some moderately separated outputs of BSS algorithm. Our preliminary motivation for such approach was the fact that BSS is usually a pre-processing step for following actual speech processors such as speaker identification and speaker verification systems where these features are necessary to be extracted; based on source-filter model of speech production, we use some of the well known speaker specific features such as mel frequency cepsteral coefficients (MFCC), Perceptual linear prediction cepsteral coefficients (PLPCC), formant frequencies (F1, F2, F3, F4) and dynamic periodicity/aperiodicity information of speech frames. We also compare theses feature sets from computational complexity point of view. II. BSS BASED TDOA ESTIMATION Fig. 1. Shows a typical block diagram for mixing and unmixing systems, in which and are stand for the impulse response of corresponding FIR filters. It is easy to see that the prefect point of separation is met when following relations hold: If we write the TDOA of each source to the sensory array as [START_REF] Buchner | Simultaneous localization of multiple sound sources using blind adaptive MIMO filtering[END_REF]:

Using (1) and ( 2), it is easy to rewrite (3) and ( 4) as:

This way, we would have the TDOA estimates of both sources along with their separated version. However the separation quality is not perfect, but still the desired source is the perceptually dominant voice in BSS output. For each array, an independent BSS algorithm is executed and using resulted separation system, a pair of TDOA estimates are calculated. Consequently we have two pairs of TDOA estimates from each array. However as a result of BSS permutation ambiguity we don't know which estimate of each TDOA pair belongs to which one of the sources. For example, if we assume the TDOA estimate from array number 1, belongs to source , we can't determine if the other TDOA estimate of from array number two is or ! In fact we need some additional information to decide about the correct pairing of estimated TDOAs from these two arrays. Fortunately BSS itself, provides separated output signals as of this required additional information. In fact, each TDOA estimate corresponds with BSS output Y i . Consequently, the correct pair of TDOAs for each speaker, corresponds with two separated outputs which are representative for the same speaker. If the separation quality was ideal, we could pair outputs with similar waveforms with basic time domain correlators. But, usually speech separation quality is not perfect and we often achieve just a moderate amount of gain in Signal to Interference Ratio (SIR). In practice, for a mixture situation, both sources would be present on both outputs but only one of them would be perceptually dominant (to some extent). Although human auditory system might percept this dominancy, the domination is not enough to let us use cross correlations for solving this global permutation ambiguity.

We propose to use speaker specific features to qualify this relative dominancy for these moderately separated outputs of BSS algorithms. Our preliminary motivation for such approach was the fact that BSS is usually a pre-processing step for following actual speech processors such as speaker identification and speaker verification systems where these features are necessary to be extracted.

Separated signals from each BSS are segmented into frames of length 30 msec. For each frame, the feature vectors are extracted and their Euclidean distance from each other is calculated. In Fig. 2, feature vectors are shown as f i and the Euclidean distance between feature vectors f i and f j is shown as " . Consequently, two decision variables are defined as:

"#$ " %" & "#$ " %" '
For each new frame, we calculate the above decision variables and decide the permutation of that frame as:

() "#$ * "#$ + # , -, ., -, / #01# , -, ., -,
Finally, we use the majority vote to decide about the permutation of the whole separated signals; each decision resembles a vote for one of possible permutations. After calculating these votes for all of the available frames, we choose the permutation which attains most of the votes.

The above discussion about solving permutation ambiguity was developed for scenario. The same procedure might be easily generalized for and scenarios. We provide the simulation results for scenario in section IV. Note that as the dimensionality of the problem increases the number of possible permutations are also increased. For example the two possible permutations for scenario is increasing to 6 possible permutations for scenario. Consequently we should define 6 decision variables like the ones in [START_REF] Campbell | Speaker recognition: A tutorial[END_REF] and [START_REF] Kokkinakis | Multichannel blind deconvolution for source separation in convolutive mixtures of speech[END_REF].

III. SPEAKER SPECIFIC FEATURES OF SPEECH SIGNALS

In this section, we provide a brief review about the nature of investigated speech features. Most of these features are based on the source-filter model of speech production. The system comprises of the vocal tract and lip radiations and depends on the physical attributes of the speakers while the source represents the pulses produced by the air flow through the vocal cords and includes such information as the fundamental frequency which is mostly influenced by the contents of the speech signal rather than physical characteristics of speakers. The speech production process is modelled as filtering of the source spectrum (glottal pulses) by the system (vocal tract) [START_REF] Maïa | Speaker Identication for Hearing Instruments[END_REF]. Depending on the nature of uttered sound, source might be composed of periodic pulses (for voiced sounds) or white noise (for unvoiced sounds).

A. Source based features

The source information of a speaker depends on factors such as the shape and timing of the glottal pulses, whether or not the vocal folds close completely and, the trade-off between the glottal source and supraglottal source during voiced obstruent sounds. Based on these factors, one can describe the way a speaker sounds in terms of the voice quality of the speaker. These speaker-specific characteristics determine (a) the high frequency roll off of the speech spectrum, (b) the relative amplitudes of the very low-frequency (c) the harmonic and inharmonic structure of the speech waveform respectively [START_REF] Espy-Wilson | A New Set of Features for Text-Independent Speaker Identification[END_REF].

The most well known source based feature is the fundamental frequency of speech signals. Although this feature is useful for gender classification but it doesn't provide enough distinctions between the members of the same gender. Although, this distinction would be met if we track the fundamental frequency changes over time instead of observing its average over all of the frames. But still, since only voiced sounds are periodic, the voiced/unvoiced decision has to be made. Also, the methods of fundamental frequency estimation are mostly prone to the presence of co channel interference of simultaneous speakers which is the case for outputs off the BSS algorithm.

For these reasons we used the A-Periodic Periodic detector (APP) introduced in [START_REF] Deshmukh | Use of Temporal Information: Detection of the Periodicity, Aperiodicity and Pitch in Speech[END_REF] in order to exploit source properties of speech signals. The summary measure defined in [START_REF] Deshmukh | Use of Temporal Information: Detection of the Periodicity, Aperiodicity and Pitch in Speech[END_REF] provides a quantified index about the amount of periodic and a-periodic energy of speech frames. It doesn't require deciding whether a given frame is voiced or unvoiced since it summarizes the amount of periodicity, i.e. the largest dip strength of Average Magnitude Difference Function (AMDF), of the envelopes of several sub-bands of speech signal (generated by auditory filter bank analysis) into summary measure index. For a strongly periodic frame, the summary measure will results in clusters across fundamental frequency and its integer multiplies; whereas for a strongly a-periodic (unvoiced) frame, the summary measure will result in dips that are randomly scattered over the range of the possible lag values with no prominent clusters. Fig. 3, presents summary measure for a strongly periodic and a strongly a-periodic frame. The use of auditory filter bank causes the summary measure to be an appropriate index which successfully reduces the effect of the non-dominant interfere in the final feature vector. However, since we expect simultaneous speakers to utter different sentences, we are sure that their corresponding summary measure would be absolutely different.

B. system based features

we have used several source based features such as formant frequencies, MFCCs and PLPCCs. The frequencies of the formants during sonorant sounds provide information about the length and shape of the vocal tract. Formants are the peaks of the spectral envelope of speech frames, which is calculated as frequency response of Linear Prediction Coefficients of order 12-14. Generally, F1 and F2 vary considerably due to the vowel being articulated, whereas F3 and F4 change very little [START_REF] Espy-Wilson | A New Set of Features for Text-Independent Speaker Identification[END_REF]. Another feature set that represents the filter characteristics of the source-filter model is the MFCC feature set. The mel frequency scale is used in order to mimic the cochlear filtering processes in the ear which places more emphasis on certain frequencies. This emphasis is done by frequency wrapping of logarithmic spectrum to the mel scale. The reference point of the mel scale is at a tone of 1000Hz. Hereafter, the mel intervals become logarithmically distributed. The mel scale was experimentally derived by measuring the difference between a linear frequency scale and the perceived pitch that human listeners registered during a series of tests [START_REF] Campbell | Speaker recognition: A tutorial[END_REF]. The MFCCs implicitly code the vocal tract information along with some information about the source and they are the most popular feature set in speaker identification systems [START_REF] Espy-Wilson | A New Set of Features for Text-Independent Speaker Identification[END_REF].

An additional feature set that implements an approximation to the human auditory system is the Perceptual Linear Prediction Cepsteral coefficients [START_REF] Campbell | Speaker recognition: A tutorial[END_REF]. PLP analysis is a combination between spectral analysis and linear prediction analysis and gives rise to modified autocorrelation coefficients, that correspond to the LPC analysis coefficients. PLP analysis consists of a preprocessing stage that not only warps the speech segments power spectrum to the Bark scale, but also applies other auditory approximations to obtain a more precise modeling of the processes in the ear such as equal loudness curve and power intensity normalization. In addition, the warping implemented here is done prior to the derivation of the AR coefficients and thus the input to the linear prediction analysis is speech that is already modified so that it contains perceptually significant information. This approximation to the biological processes that are executed in the human ear and the consequent smoothing of the spectrum is proven to be helpful in the effective discernment between different speakers. In fact, PLP is reported to be more robust against noisy observations [START_REF] Campbell | Speaker recognition: A tutorial[END_REF].

IV. SIMULATION RESULTS

In this section we compare the feasibility of using each feature set for resolving permutation ambiguity, on a large database of different speech sources. We use ROOMSIM [START_REF] Campbell | A MATLAB[END_REF] toolbox in order to simulate the configuration of 2 sources and two sets of microphone arrays as shown in Fig. 3. ROOMSIM provides us with the impulse responses between each source and microphone pairs. First, We apply the BSS algorithm of [START_REF] Kokkinakis | Multichannel blind deconvolution for source separation in convolutive mixtures of speech[END_REF] to the observed mixtures of two speech signals in order to obtain the separation system impulse responses; then, we apply this exact separation system to all the other mixtures generated by different pairs of speech signals which are randomly selected from the database. The reason for using these fixed impulse responses for all of the source pairs is to test them under equal conditions. Table I, summarizes the achieved SIR gains of obtained separating system. [START_REF] Syskind Pedersen | A survey of convolutive blind source separation methods[END_REF] which implies that the reported SIR gains in table I are reasonable values for testing the ability of feature sets to resolve permutation ambiguity. Our database of speech signals consists of 15 male and 15 female voices of length 3 seconds.

The frame length is chosen to be 30 milliseconds which is equal to the stationary period of speech signal and is an appropriate choice for periodic/a-periodic analysis of frames. We use summary measure, 13 th order MFC and PLP coefficients and the first four formants: F1, F2, F3 and F4 as feature sets. Also the cross correlation decision making procedure introduced in [START_REF] Lombard | Multidimensional Localization of Multiple Sound Sources Using Blind Adaptive MIMO System Identification[END_REF] is used to compare its results with feature based ambiguity resolver for 250 different signal pairs which are randomly chosen from the database.

Table II summarizes the averages precent of correct decisions for each one of these feature sets. Also in order to evaluate the stability of each feature set, the standard deviation of correct decisions is reported as well along with the total number of complete failures; by failure we mean the vote below 50% which implies the wrong choice of permutations. Table II, reveals that the most robust feature set is the summary measure. This is because this measure explicitly tracks dynamics of speech signal which are mostly caused by source properties of speech production model. Also, this measure successfully reduces the effect of mask (interferer) signal by using auditory filter bank analysis.

The same statement could be made for MFCC and PLPCC feature sets, since they also employ human like pre-processing. Although, they contain the information about both source and the vocal tract properties and this might be the reason for their suboptimal results compared to summary measure which solely represents source dynamics. In fact, the vocal tract filter properties (the spectral envelope of frames) are very likely to be overridden by frequency response of too many FIR filters (of mixing and unmixing systems) that are on the way of BSS outputs. So we don't expect system based features to be as distinctive as they are for pure speech signals.

Specially, the obtained results of formant feature set proves this rationalization. Also, The cross correlation decision making obtained the worst results and we can say that it's an completely inappropriate criteria for permutation ambiguity resolving. Practically, we observed that this criteria only works well when the mixed speeches are as non overlapping as possible in time domain. When high intensity portions of two speech signals occur simultaneously, this measure is very likely to fail.

We must also mention the computational complexity of computing each feature set. Also, another experiment was done for scenario. 300 different combinations of 3 speakers was chosen from the database. Under the same conditions as previous experiment, the generalization of the decision logic in section IV was used to solve permutation ambiguity. Table VI summarizes the averages precent of correct decisions for each one of the feature sets. Note that, since the number of possible permutations are increased from 2 in scenario to 6 in scanario, we expect the increase of false decisions. As table VI suggests, formants and cross correlations are completely wrong in their decisions because of their less than 50% average vote. Also, despite the very good performance of summary measure in scenario, it demonstrates very poor results in case. But MFCC and PLPCC coefficients still show acceptable majority votes.

V. CONCLUSION In order to solve global permutation ambiguity of moderately separated outputs of BSS, we used several speaker specific features of speech We employed some of the most popular feature sets which are used in system identification systems. Among them, scenario the summary measure over performed other filter based features such as MFCC, PLPCC and formants, but if we take execution runtime into account, we may say that MFCC and PLPCC are the most preferred ones. But in scenario, summary measure completely fails in making correct decisions just like the formants and cross correlations. However, PLPC and MFCC features are still reliable for solving the ambiguity problem.

Fig. 1 .

 1 Fig. 1. Block diagram of mixing and unmixing systems.

Fig 2 .

 2 Fig 2. Shows the overall proposed block diagram for Localization of two speakers. Two sets of microphone arrays are used to record sounds.For each array, an independent BSS algorithm is executed and using resulted separation system, a pair of TDOA estimates are calculated. Consequently we have two pairs of TDOA estimates from each array. However as a result of BSS permutation ambiguity we don't know which estimate of each TDOA pair belongs to which one of the sources.

Fig. 2 .

 2 Fig. 2. the proposed block diagram for Localization of two speakers.

Fig. 2 .

 2 Fig. 2. The resultant summary measure for (up) a strongly periodic frame where it consists of strong clusters around fundamental frequency and its integer multiplies and (down) an strongly a-periodic frame where weak dips are randomly scattered over the range of all possible lag values [6].

Fig. 3 .

 3 Fig. 3. The configuration of two microphone arrays and a pair of active speakers simulated using RoomSim [10].

TABLE II THE

 II RESULTED AVERAGE DECISION STATISTICS.

	Feature set	Correct votes	Standard deviation	Number of failures
	Cross correlation [4]	59%	10.4%	24
	MFCC	80.6%	6.2 %	0
	PLPCC
	Formants	62.41%	6.25%	4
	Summary measure	87.2%	4.47%	0

  Table III reports the average runtime for accomplishment of one decision (for one 30 ms frame). It reveals that, despite the very good performance of summary measure it's runtime is several times of the other feature sets. If the runtime is an important design parameter, then one could prefer to use PLPCC or MFCC feature sets which are almost as distinctive as summary measure for our problem in hand.

TABLE III AVERAGE

 III RUNTIME FOR ACCOMPLISHMENT OF ONE DECISION.

	Feature set	Average runtime (seconds)
	Cross correlation [4]	0.0052
	MFCC	0.0077
	PLPCC	0.0071
	Formants	0.03
	Summary measure	4.95

978-1-4244-4683-4/09/$25.00 ©2009 IEEE

IEEE Symposium on Industrial Electronics and Applications (ISIEA 2009), October 4-6, 2009, Kuala Lumpur, Malaysia