Deep Neural Networks and End-to-End Learning for Audio Compression

Rim, Daniela N.; Jang, Inseon; Choi, Heeyoul

doi:10.5626/jok.2021.48.8.940

Cited by 9 publications

(1 citation statement)

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“…The compression in several aspects depends on the quality and type of data, and coding design [1], [2]. Information compression is intriguing in transmission information preparation, both because of the cost savings it offers and the expansive volume of information controlled in numerous transmission applications [3]- [6]. The sorts of the nearby excess shown in transmission information records incorporate runs of zeros in numeric areas and groupings of spaces in alphanumeric areas which are displayed in a few records and invalid in others.…”

Section: Introductionmentioning

confidence: 99%

Effective Audio Compression using Enhanced Encoding Method

2024

IJSER

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The rapid growth of big data and the emergence of new technologies have posed significant challenges in data compression, particularly in the domain of audio compression. Audio compression has gained widespread applications in advanced audio coding, MP3 encoding, web radio, and lossless audio coding techniques. In this paper, a newly proposed technique is present aimed at improving audio compression. The proposed technique consists of two main stages: preprocessing and the application of compression techniques. The preprocessing stage involves several steps. Firstly, the raw audio data is subjected to set normalization to ensure consistent scaling. Next, the data is divided into smaller segments to facilitate the application of Discrete Cosine Transform (DCT) and quantization on each segment. The quantization parameter is determined based on the bit per sample of the audio test file and the number of channels. To implement the Modified MLZW compression technique, the data is converted into byte values, and a word reference is constructed based on these bytes. To evaluate the performance of the proposed technique, multiple audio files have been utilized in experimental tests. The results demonstrate that the proposed technique achieves smaller MSE of 2.05*10 -10 and significant improvements in terms of compression efficiency with reduction in processing time to 200 ms. The proposed methods works better and ensure that the audio quality is good and the system can be used for transmitting audio over different media that depend on lossy audio compression.

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