In this paper, we propose a new joint transmit and receive spatial/frequency-domain filtering for single-carrier (SC) multiple-input multiple-output (MIMO) eigenmode transmission using iterative interference cancellation (IC). Iterative IC is introduced to a previously proposed joint transmit and receive spatial/frequency-domain filtering based on minimum mean square error criterion (called joint Tx/Rx MMSE filtering) to reduce the residual inter-symbol interference (ISI) after the Rx filtering. The optimal Tx/Rx filters are derived based on the MMSE criterion taking into account the iterative IC. The superiority of our proposed technique is confirmed by computer simulation.

The noise robustness of an audio fingerprinting system is one of the most important issues in music information retrieval by the content-based audio identification technique. In a real environment, sound recordings are commonly distorted by channel and background noise. Recently, Philips published a robust and efficient audio fingerprinting system for audio identification. To extract a robust and efficient audio fingerprint, Philips applied the first derivative (differential) to the frequency-time sequence of the perceptual filter-bank energies. In practice, however, the noise robustness of Philips' audio fingerprinting scheme is still insufficient. In this paper, we introduce an extension method of the audio fingerprinting scheme for the enhancement of noise robustness. As an alternative to frequency filtering, a type of band-pass filter, instead of a high-pass filter, is used to achieve robustness to background noise in a real situation. Our experimental results show that the proposed filter improves the noise robustness in audio identification.

Jacobian Adaptation (JA) has been successfully used in Automatic Speech Recognition (ASR) systems to adapt the acoustic models from the training to the testing noise conditions. In this work we present an improvement of JA for speaker verification, where a specific training noise reference is estimated for each speaker model. The new proposal, which will be referred to as Model-dependent Noise Reference Jacobian Adaptation (MNRJA), has consistently outperformed JA in our speaker verification experiments.

There have been numerous studies on the enhancement of the noisy speech signal. In this paper, We propose a new speech enhancement method, that is, a DFF (Dissonant Frequency Filtering) scheme combined with NR (noise reduction) algorithm. The simulation results indicate that the proposed method provides a significant gain in perceptual quality compared with the conventional method. Therefore if the proposed enhancement scheme is used as a pre-filter, the output speech quality would be enhanced perceptually.