Acoustic Parameter Extraction From Occupied Rooms Utilizing Blind Source Separation
Room acoustic parameters such as reverberation time (RT) can be extracted from passively received speech signals by certain ‘blind’ methods, thereby mitigating the need for good controlled excitation signals or prior information of the room geometry. Observation noise which is inevitable in occupied rooms will, however, degrade such methods greatly. In this chapter, a new noise reducing preprocessing which utilizes blind source separation (BSS) and adaptive noise cancellation (ANC) is proposed to reduce the unknown noise from the passively received reverberant speech signal, so that more accurate room acoustic parameters can be extracted. As a demonstration this noise reducing preprocessing is utilized in combination with a maximum-likelihood estimation (MLE)-based method to estimate the RT of a synthetic noise room. Simulation results show that the proposed new approach can improve the accuracy of the RT estimation in a simulated high noise environment. The potential application of the proposed approach for realistic acoustic environments is also discussed, which motivates the need for further development of more sophisticated frequency domain BSS algorithms.
KeywordsSpeech Signal Blind Source Separation Acoustic Parameter Less Mean Square Algorithm Reverberation Time
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