Download KREST ADAPTIVE ALGORITHM FOR SPEECH COMPRESSION

ADAPTIVE ALGORITHM FOR SPEECH COMPRESSION USING
COSINE PACKET TRANSFORM
SYNOPSIS
This project presents a new adaptive algorithm for speech Compression using Cosine
Packet Transform. The proposed algorithm uses packet decomposition, which reduces a
computational complexity of a system. This paper compare the compression ratio of methods
using Wavelet Transform, Cosine Transform, Wavelet Packet Transform and proposed adaptive
algorithm using Cosine Packet Transform for different speech signal samples. The mean
compression ratio is calculated for all the methods and compared. The implemented results show
that the proposed compression algorithm gives the better performance for speech signals.
With rapid deployment of speech compression technologies, more and more speech
content is stored and transmitted in compressed formats. Speech signals has unique properties
that differ from a general audio/music signals. First, speech is a signal that is more structured and
band-limited around 4 kHz. These two facts can be exploited through different models and
approaches and at the end, make it easier to compress. Today, applications of speech
compression involve real time processing in mobile satellite communications, cellular telephony,
internet telephony, audio for videophones or video teleconferencing systems, among others.
Other applications include also storage and synthesis systems used, for example, in voice mail
systems, voice memo wristwatches, voice logging recorders and interactive PC software. The
idea of speech compression is to compress speech signal to take up less storage space and less
bandwidth for transmission. To meet this goal different methods for compression have been
designed and developed by various researchers. The speech compression is used in digital
telephony, in multimedia and in the security of digital communications. Before the introduction
of Packet based transform techniques, audio coding techniques used DFT and DCT with window
functions such as rectangular and sine-taper functions.
Head office: 2nd floor, Solitaire plaza, beside Image Hospital, Ameerpet, Hyderabad
www.kresttechnology.com, E-Mail: [email protected] , Ph: 9885112363 / 040 44433434
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However, these early coding techniques have failed to full fill the contradictory
requirements imposed by high-quality audio coding. For example, with a rectangular window the
analysis/synthesis system is critically sampled, i.e., the overall number of the transformed
domain samples is equal to the number of time domain samples, but the system suffers from poor
frequency resolution and block effects, which are introduced after quantization or other
manipulation in the frequency domain. Overlapped windows allow for better frequency response
functions but carry the penalty of additional values in the frequency domain, thus not critically
sampled. Discrete Cosine Packet Transform is currently the best solution, which has
satisfactorily solved the paradox. Speech compressions are done by either based on linear
prediction or based on orthogonal transforms methods. On the basis of the classical papers
written by Shannon and Kolmogorov, recently was highlighted a strong connection between the
systems proposed in many lossy compression standards and the harmonic analysis. All these
systems use orthogonal transforms. The algorithm described in this paper belongs to the second
category. Unfortunately there is no any fast algorithm for the computation of orthogonal
transform. This is the reason why in practice other orthogonal transforms are used. The quality of
compression system can be appreciated with the aid of his rate distortion function. A
compression system is better than another if, at equal distortions; it realizes a higher compression
rate. The maximization of compression rate can be done, if a good selection of orthogonal
transform be made.
Head office: 2nd floor, Solitaire plaza, beside Image Hospital, Ameerpet, Hyderabad
www.kresttechnology.com, E-Mail: [email protected] , Ph: 9885112363 / 040 44433434
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