A High-quality Audio Data Decompression Algorithm and Fast Reconstruction Technology Based on Artificial Intelligence Algorithm

In the context of the continuous development of the streaming media era, traditional audio technology can no longer meet the needs of many fields, especially the needs of music production and broadcasting, games, video and other fields. Based on this, this paper proposes a high-quality audio data decompression algorithm and fast reconstruction technology based on artificial intelligence algorithms, which greatly improves the audio decompression performance based on the optimization algorithm and system architecture. During the experiment, the average decompression time of the system was reduced from 12 seconds to 7 seconds, an increase of 41.67%. The sound quality score has been improved by 5% from 87 to 92. In addition, bandwidth consumption and storage usage are reduced by 30% and 8.33%, respectively, effectively reducing data transmission and storage costs. The processing delay has also been reduced from 100ms to 50ms, greatly improving the system response speed and user experience. Based on the above optimization, the AI intelligent sound system constructed in this paper has achieved significant improvement in several key performance indicators, indicating that this method has high application value and prospect in the field of audio data decompression and reconstruction.

Authors

Institutions

Publication Details

Journal
Procedia Computer Science
Published
2025-01-01
DOI
https://doi.org/10.1016/j.procs.2025.04.254
Primary Topic
Music and Audio Processing
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A High-quality Audio Data Decompression Algorithm and Fast Reconstruction Technology Based on Artificial Intelligence Algorithm

Chen Wang, Xin'ang Jiang
Procedia Computer Science
Music and Audio Processing
article

A High-quality Audio Data Decompression Algorithm and Fast Reconstruction Technology Based on Artificial Intelligence Algorithm

Chen Wang, Xin'ang Jiang
article en

Abstract

In the context of the continuous development of the streaming media era, traditional audio technology can no longer meet the needs of many fields, especially the needs of music production and broadcasting, games, video and other fields. Based on this, this paper proposes a high-quality audio data decompression algorithm and fast reconstruction technology based on artificial intelligence algorithms, which greatly improves the audio decompression performance based on the optimization algorithm and system architecture. During the experiment, the average decompression time of the system was reduced from 12 seconds to 7 seconds, an increase of 41.67%. The sound quality score has been improved by 5% from 87 to 92. In addition, bandwidth consumption and storage usage are reduced by 30% and 8.33%, respectively, effectively reducing data transmission and storage costs. The processing delay has also been reduced from 100ms to 50ms, greatly improving the system response speed and user experience. Based on the above optimization, the AI intelligent sound system constructed in this paper has achieved significant improvement in several key performance indicators, indicating that this method has high application value and prospect in the field of audio data decompression and reconstruction.

Procedia Computer ScienceVol. 261
Changchun Discovery Sciences (China) (CN), Changchun Children's Hospital (CN)
Sustainable cities and communities
Openalex Percentile: Top 87%
Music and Audio Processing
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.