Speech Compression using Multi-Resolution Hybrid Wavelet using DCT and Walsh Transforms

Vig, Rekha; Chauhan, Sachin

doi:10.1016/j.procs.2018.05.070

Cited by 8 publications

(5 citation statements)

References 2 publications

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“…Compression ratio is conducted to measures the overall performance of the suggested speech compression system. Compression factor (C) is used to assess the reconstructed signal's performance [13].…”

Section: Performance Evaluationmentioning

confidence: 99%

Speech Coding Using Discrete Cosine Transform and Chaotic Map

Jamal¹,

Hassan²

2022

ISI

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Recently, data of multimedia performs an exponentially blowing tendency, saturating daily life of humans. Various modalities of data, includes images, texts and video, plays important role in different aspects and has wide. However, the key problem of utilizing data of large scale is cost of processing and massive storage. Therefore, for efficient communications and for economical storage requires effective techniques of data compression to reduce the volume of data. Speech coding is a main problem in the area of digital speech processing. The process of converting the voice signals into a more compressed form is speech coding. In this work, we demonstrate that a DCT with a chaotic system combined with run-length coding can be utilized to implement speech coding of very low bit-rate with high reconstruction quality. Experimental result show that compression ratio is about 13% when implemented on Librispeech dataset.

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Section: Performance Evaluationmentioning

confidence: 99%

Speech Coding Using Discrete Cosine Transform and Chaotic Map

Jamal¹,

Hassan²

2022

ISI

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“…For audio compression, the DWT is better than DCT. Viga and Chauhan [15] proposed a hybrid wavelet for speech compression. The percentage of energy to be kept are changing to implement different levels of compression, this is used as a threshold.…”

Section: Introductionmentioning

confidence: 99%

Audio compression using transforms and high order entropy encoding

Ahmed

George

Hadi

2021

IJECE

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<span>Digital audio is required to transmit large sizes of audio information through the most common communication systems; in turn this leads to more challenges in both storage and archieving. In this paper, an efficient audio compressive scheme is proposed, it depends on combined transform coding scheme; it is consist of i) bi-orthogonal (tab 9/7) wavelet transform to decompose the audio signal into low & multi high sub-bands, ii) then the produced sub-bands passed through DCT to de-correlate the signal, iii) the product of the combined transform stage is passed through progressive hierarchical quantization, then traditional run-length encoding (RLE), iv) and finally LZW coding to generate the output mate bitstream. The measures Peak signal-to-noise ratio (PSNR) and compression ratio (CR) were used to conduct a comparative analysis for the performance of the whole system. Many audio test samples were utilized to test the performance behavior; the used samples have various sizes and vary in features. The simulation results appear the efficiency of these combined transforms when using LZW within the domain of data compression. The compression results are encouraging and show a remarkable reduction in audio file size with good fidelity.</span>

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“…There are two main techniques used to compress the data: lossless compression and lossy compression [4]. Compression of speech is achieved by neglecting and discarding small and lesser coefficients and data and then using quantizing and encryption techniques without significant loss of speech intelligibility [3,5]. Lossless compression reversibly encodes data, while lossy compression removes perceptually less significant information [6].…”

Section: Introductionmentioning

confidence: 99%

“…The method contains many steps to prepare the speech signal to make it more comparable to the original JPEG technique. Vig and Chauhan [5], use a hybrid multi-resolution wavelet for speech signals with variable duration for compression. This hybrid wavelet construction uses two transforms (discrete cosine transform-DCT and Walsh).…”

Section: Introductionmentioning

confidence: 99%

Speech signal compression and encryption based on sudoku, fuzzy C-means and threefish cipher

Abduljaleel

Khaleel

2021

IJECE

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<span lang="EN-US">Compression and encryption of speech signals are essential multimedia technologies. In the field of speech, these technologies are needed to meet the security and confidentiality of information requirements for transferring huge speech signals via a network, and for decreasing storage space for rapid retrieval. In this paper, we propose an algorithm that includes hybrid transformation in order to analyses the speech signal frequencies. The speech signal is then compressed, after removing low and less intense frequencies, to produce a well compressed speech signal and ensure the quality of the speech. The resulting compressed speech is then used as an input in a scrambling algorithm that was proposed on two levels. One of these is an external scramble that works on mixing up the segments of speech that were divided using Fuzzy C-Means and changing their locations. The internal scramble scatters the values of each block internally based on the pattern of a Sudoku puzzle and quadratic map so that the resulting speech is an input to a proposed encryption algorithm using the threefish algorithm. The proposed algorithm proved to be highly efficient in the compression and encryption of the speech signal based on approved statistical measures.</span>

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Speech Compression using Multi-Resolution Hybrid Wavelet using DCT and Walsh Transforms

Cited by 8 publications

References 2 publications

Speech Coding Using Discrete Cosine Transform and Chaotic Map

Speech Coding Using Discrete Cosine Transform and Chaotic Map

Audio compression using transforms and high order entropy encoding

Speech signal compression and encryption based on sudoku, fuzzy C-means and threefish cipher

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