A High-quality Audio Data Decompression Algorithm and Fast Reconstruction Technology Based on Artificial Intelligence Algorithm
Chen Wang, Xinli Jiang · Procedia Computer Science · 2025
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.