Figure 2.A typical one end telephone speech.In the absence of speech, the primary input of the adaptive filter could be used as a reference signal for the present noise signal to adapt the filter coefficients using any type of adaptive algorithms. In this context, the least mean squares (LMS) system is commonly used for its robustness and simplicity. The LMS buy inhibitor is a gradient search algorithm that seeks an optimum on quadratic surface. Detailed discussion and derivation of the LMS algorithm can be found in many references (e.g., [7]). The noise in the reference microphone of the ANC of Figure 1 should be a very close estimate of the noise component in the speech signal. If a speech signal is then detected, the VAD switches the reference input back to the reference sensor.
The adaptive filter in the LMS system should now have the same characteristics as the noise path so that the noise is reduced to a minimum. Furthermore, the VAD freezes the filter adaptation when speech is present so that the target speech is not reduced. In the literature, several VAD schemes have been introduced, each providing a solution to a certain aspect of the problem. The main issues of VADs are threshold control [8], computational complexity [9] and robustness [10]. In the current work, a VAD and an adaptive noise canceller are made to have a mutual control so that an improved noise cancellation performance is obtained. The paper is organized as follows. In addition to this introductory section, Section 2 presents a review of VAD techniques, Section 3 gives a general description of the proposed VAD algorithm, Section 4 gives details of the features used in the proposed voice activity detector.
In Section 5, the mutual control between the VAD and the adaptive noise canceller is explained. Section 6 gives a description of the adaptive Brefeldin_A noise canceller used in this work. Section 7 presents a performance evaluation Pacritinib with a discussion of the results of the developed noise cancellation system, and Section 8 concludes the paper with the main aspects of the research.2.?A Review of Voice Activity Detection TechniquesThe process of detecting the presence of speech/non-speech is not a fully resolved problem in speech processing systems. Numerous applications such as robust speech recognition [11,12], real-time speech transmission on the Internet [13], noise reduction and echo cancellation schemes in telecommunication systems are affected by such a process [14,15]. The detection of speech/non-speech is not an easy task as it may look. Most VAD algorithms fail to function properly when the level of background noise becomes severely high. During the last decade, many researchers have developed different techniques such as those found in [16�C18] for detecting speech on a noisy signal.