This paper focuses on cascaded approaches to non-linear acoustic echo cancellation (AEC) for mobile communications. The contributions in this paper are two-fold. They relate (i) to computationally efficient pre-processing and clipping compensation which aims to improve non-linear modelling and (ii) decorrelation filtering which aims to improve the tracking performance of a conventional linear AEC algorithm. While well-established in the literature the two modules require significant development in order that they function coherently in a cascaded approach. This paper presents new, adaptive parameterisation procedures for both modules and demonstrates significant improvements in terms of echo return loss enhancement when the two modules are combined.

An improvement of the speech spectrogram based on the method of reassignment is presented. This method consists of moving each point of the spectrogram to a new point that represents the distribution of the energy in the time-frequency window more accurately. Examples of natural speech show an improvement of the energy localization in both time and frequency domains. This allows a better description of speech features.

Modulation maps provide an effective method for the segregation of voiced speech sounds from competing background activity. The maps are constructed by computing modulation spectra in a bank of auditory filters. If the modulation spectra are computed using a conventional DFT, windows of 200 ms duration are necessary. The reassigned spectrum, a new time frequency representation (Kodera et al., 1978, and Auger and Flandrin, 1995), allows a reduction in the window size to 50 ms without loss of performance. The algorithm is tested on a 'double vowel' identification task that has been used extensively in psychophysical experiments.

Sinusoidal transform coding (STC) techniques model speech as the sum of sine-waves whose frequencies, amplitudes and phases are specified at regular intervals. To achieve a low-bit rate representation, only the spectral envelope is encoded and the phases are regenerated according to a minimum phase assumption. In this paper, the inaccuracy of the minimum phase model is demonstrated. It is shown that the phase spectra of decoded speech segments may be corrected using either the parameters of a Rosenberg pulse model or a second order all-pass filter. Experiments have shown that by applying this correction, the phase accuracy increases and the speech quality improves.