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Some Speech Enhancement algorithms based on the iterative Wiener filtering Method due to LimOppenheim [2] are presented. In the original Lim-Oppenheim algorithm, AR spectral estimation of speech is carried out using a second-order analysis, but our algorithms consider an AR estimation by means of cumulant analysis. This work extends some preceding papers due to the authors. Information of previous speech frames is taken to initiate speech AR modeling of the current frame and, so, two parameters are introduced to dessign Wiener Filter at first iteration of every frame. Another algorithm obtains speech AR estimation in the autocorrelation domain. Both algorithms are compared to classical second-order algorithm (AR2) and third-onler cumulant-based algorithm (AR3), when car noise disturbs clean speech signal. A detailed study shows that boths techniques significantly increase noise suppression after first iteration processing and, therefore, convergence speed of this iterative algorithm is strongly accelerated., Peer Reviewed, Postprint (published version)

This paper describes a coding scheme for broadband speech. It can be seen as a vectorial extension of a conventional ADPCM encoder. In this scheme, signal vector is formed with one sample of the normalized prediction error of each subband and then it is vector quantized. It combines the advantages of the scalar prediction and those of vector quantization (VQ). We handle the high vector dimensionality by using a multiVQ. It requires a previous subvector division and an adequate bit assignment among them. This scheme shows a high capacity to drive large dynamic range signals like broadband speech. Predictor and codebook dessigns are discussed. Some results about speech prediction and coding are reported., Peer Reviewed, Postprint (published version)