nerv.speech_utils = {}

function nerv.speech_utils.global_transf(feat_utter, global_transf,
                                        frm_ext, frm_trim, gconf)
    -- prepare for transf
    local input = {feat_utter}
    local output = {feat_utter:create()}
    -- do transf
    global_transf:init(input[1]:nrow())
    global_transf:propagate(input, output)
    -- trim frames
    expanded = gconf.cumat_type(output[1]:nrow() - frm_trim * 2, output[1]:ncol())
    expanded:copy_fromd(output[1], frm_trim, feat_utter:nrow() - frm_trim)
    collectgarbage("collect")
    return expanded
end

function nerv.speech_utils.feat_expand(feat_utter, frm_ext, gconf)
	local rearranged
	if frm_ext > 0 then
        	local step = frm_ext * 2 + 1 
        	-- expand the feature
        	 local expanded = gconf.mmat_type(feat_utter:nrow(), feat_utter:ncol() * step)	
		 expanded:expand_frm(feat_utter, frm_ext)

		-- rearrange the feature (``transpose'' operation in TNet)
		rearranged = gconf.mmat_type(feat_utter:nrow() - frm_ext*2, feat_utter:ncol() * step)
        	rearranged:rearrange_frm(expanded, step, frm_ext, feat_utter:nrow() - frm_ext)
	else
		rearranged = feat_utter
	end

	collectgarbage("collect")
    	return rearranged 
end


function nerv.speech_utils.normalize(mat, global_transf)
    	-- prepare for transf
    	local input = {mat}
    	local output = {mat:create()}
    	-- do transf
    	global_transf:init(input[1]:nrow())
    	global_transf:propagate(input, output)
	
	return output[1]
end