In this era of ever-improving communications technologies, we have become used to conversing with others across the globe. Invariably, a real-time telephone conversation begins with a microphone or other audio recording device. Noise in the environment can corrupt our speech signal as it is being recorded, making it harder to both use and understand further down the communications pathway. Other talkers in the environment add their own auditory interference to the conversation. Recent work in advanced signal processing has resulted in new and promising technologies for recovering speech signals that have been corrupted by speech-like and other types of interference. Termed blind source separation methods, or BSS methods for short, these techniques rely on the diversity provided by the collection of multichannel data by an array of distant microphones (sensors) in room environments. The practical goal of these methods is to produce a set of output signals which are much more intelligible and listenable than the mixture signals, without any prior information about the signals being separated, the room reverberation characteristics, or the room impulse response.
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Douglas, S.C., Gupta, M. (2007). Convolutive Blind Source Separation for Audio Signals. In: Makino, S., Sawada, H., Lee, TW. (eds) Blind Speech Separation. Signals and Communication Technology. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-6479-1_1
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