03_T170_vr-vijay - ELECTRONICS AND ELECTRICAL ENGINEERING...

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17 ELECTRONICS AND ELECTRICAL ENGINEERING ISSN 1392 – 1215 2007. No. 2(74) ELEKTRONIKA IR ELEKTROTECHNIKA ELECTRONICS T 170 ELEKTRONIKA Modified Adaptive Filtering Algorithm for Noise Cancellation in Speech Signals V. R. Vijaykumar, P. T. Vanathi Department of Electronics and Communication Engineering, PSG College of Technology, Coimbatore-641004, India, tel.: -0091-9442014139, e-mail: [email protected], [email protected] P. Kanagasapabathy Madras Institute of Technology, Chennai, India, email: [email protected] Introduction Speech is a very basic way for humans to convey information to one another with a bandwidth of only 4 kHz; speech can convey information with the emotion of a human voice. The speech signal has certain properties: It is a one-dimensional signal, with time as its independent variable, it is random in nature, it is non-stationary, i.e. the frequency spectrum is not constant in time. Although human beings have an audible frequency range of 20Hz to 20 kHz, the human speech has significant frequency components only up to 4 kHz. The most common problem in speech processing is the effect of interference noise in speech signals. Interference noise masks the speech signal and reduces its intelligibility. Interference noise can come from acoustical sources such as ventilation equipment, traffic, crowds and commonly, reverberation and echoes. It can also arise electronically from thermal noise, tape hiss or distortion products. If the sound system has unusually large peaks in its frequency response, the speech signal can even end up masking itself. One relationship between the strength of the speech signal and the masking sound is called the signal-to-noise ratio, expressed in decibels. Ideally, the S/N ratio is greater than 0dB, indicating that the speech is louder than the noise. Just how much louder the speech needs to be in order to be understood varies with, among other things, the type and spectral content of the masking noise. The most uniformly effective mask is broadband noise. Although, narrow-band noise is less effective at masking speech than broadband noise, the degree of masking varies with frequency. High-frequency noise masks only the consonants, and its effectiveness as a mask decreases as the noise gets louder. But low-frequency noise is a much more effective mask when the noise is louder than the speech signal, and at high sound pressure levels it masks both vowels and consonants. In general, noise [2] that affects the speech signals can be modeled using any one of the following: 1. White noise, 2. Colored noise, 3. Impulsive noise. White noise White noise is a sound or signal consisting of all audible frequencies with equal intensity. At each frequency, the phase of the noise spectrum is totally uncertain: It can be any value between 0 and 2 π , and its value at any frequency is unrelated to the phase at any other frequency. When noise signals arising from two different sources add, the resultant noise signal has a power equal to the sum of the component powers. Because of the broad-band spectrum, white noise has strong masking capabilities.
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