Speech Compression Using Wavelets
Speech Compression Using Wavelets
Team Members:
Deepak Kumar,
3rd B-tech,
E.C.E Branch,
JNTU College of Engineering,
Kakinada.
E-Mail: deepakjntu427@yahoo.com
Ch. Naresh Kumar,
3rd B-Tech,
E.C.E Branch,
JNTU College of Engineering,
Kakinada.
ABSTRACT
Speech compression is the technology of converting human speech into an efficiently encoded representation that can later be decoded to produce a close approximation of the original signal. The wavelet transform of a signal decomposes the original signal into wavelets coefficients at different scales and positions. These coefficients represent the signal in the wavelet domain and all data operations can be performed using just the corresponding wavelet coefficients. The major issues concerning the design of this Wavelet based speech coder are choosing optimal wavelets for speech signals, decomposition level in the DWT, thresholding criteria for coefficient truncation and efficient encoding of truncated coefficients. The performance of the wavelet compression scheme on both male and female spoken sentences is compared. On a male spoken sentence the scheme reaches a signal-to-noise ratio of 17.45 db and a compression ratio of 3.88, using a level dependent thresholding approach.
1. 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. People want to be able to hear someone’s voice from anywhere in the world as if the person was in the same room .As a result a greater emphasis is being placed on the design of new and efficient speech coders for voice communication and transmission. Today applications of speech coding and compression have become very numerous. This paper looks at a new technique for analyzing and compressing speech signals using wavelets. Any signal can be represented by a set of scaled and translated versions of a basic function called the. mother
Team Members:
Deepak Kumar,
3rd B-tech,
E.C.E Branch,
JNTU College of Engineering,
Kakinada.
E-Mail: deepakjntu427@yahoo.com
Ch. Naresh Kumar,
3rd B-Tech,
E.C.E Branch,
JNTU College of Engineering,
Kakinada.
ABSTRACT
Speech compression is the technology of converting human speech into an efficiently encoded representation that can later be decoded to produce a close approximation of the original signal. The wavelet transform of a signal decomposes the original signal into wavelets coefficients at different scales and positions. These coefficients represent the signal in the wavelet domain and all data operations can be performed using just the corresponding wavelet coefficients. The major issues concerning the design of this Wavelet based speech coder are choosing optimal wavelets for speech signals, decomposition level in the DWT, thresholding criteria for coefficient truncation and efficient encoding of truncated coefficients. The performance of the wavelet compression scheme on both male and female spoken sentences is compared. On a male spoken sentence the scheme reaches a signal-to-noise ratio of 17.45 db and a compression ratio of 3.88, using a level dependent thresholding approach.
1. 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. People want to be able to hear someone’s voice from anywhere in the world as if the person was in the same room .As a result a greater emphasis is being placed on the design of new and efficient speech coders for voice communication and transmission. Today applications of speech coding and compression have become very numerous. This paper looks at a new technique for analyzing and compressing speech signals using wavelets. Any signal can be represented by a set of scaled and translated versions of a basic function called the. mother
References: [1]. A. Chen, N. Shehad, A. Virani and E. Welsh, Discrete Wavelet Transform for Audio Compression, (current July. 16, 2001). [2]. Speech Compression Using Wavelets by Nikhil Rao [3]. S.Haykin, Communication Systems, John Wiley & Sons, New York, 2001.