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Wireless @ KTH Research Project Proposal : Single and Multi-Channel Noise Reduction Based on Blind Source Separation

Advances in telecommunications over the last few decades have made communications anywhere a reality. Technological progress has made communication systems reliable and affordable and mobile communication has now become ubiquitous. The freedom and flexibility provided by mobile communications introduces new challenges in the area of voice communication, an important one being the suppression of background acoustic noise. Mobile users communicate with one another in different environments with varying amounts and types of background noise. Noise reduction is a vital component to improve the quality and intelligibility of the transmitted speech. While speech recognition systems and speaker identification/verification systems have attained a high level of maturity under controlled noiseless conditions, one of the main reasons for the lack of their widespread deployment is poor performance in practical noisy environments. Providing environmental robustness is envisioned to have a potentially dramatic influence on the role of voice services in future wireless communications. Noise reduction remains a challenging problem largely due to the wide variety of background noise types and the difficulty in estimating their statistics. Examples of noise types include traffic noise in cities, multi-talker babble noise in cafeterias, noise in subways etc, which are commonly encountered by end-users of wireless communications. Traditional noise suppression techniques fall into the category of single-channel algorithms that have only a single microphone to obtain the input signal, and are thus attractive in mobile applications due to cost, size and battery life factors. A drawback is that noise estimates need to be obtained from the noisy observation. This has proved to be a particularly difficult task, especially in nonstationary noise conditions. Most noise estimation methods typically employ a buffer of past noisy data from which estimates are obtained. While on the one hand large buffers are required for accurate estimates, very large buffers on the other hand make it difficult to deal with changing noise, which is the case in the practical scenarios mentioned earlier. Multi-channel noise reduction systems offer a promising alternative. By using more than one microphone, it is possible to suppress noise using techniques such as adaptive beamforming. While the problem of having to estimate the noise statistics from a single noisy observation is eliminated, additional microphones are expensive and may not be suitable for all applications. Recent trends however indicate that with falling costs and more compact microphones, multi-channel techniques are becoming more practical. By combining aspects of both single and multiple channel methods, we expect to develop a robust system that provides superior performance in different noise environments and nonstationary noise conditions.