CELP Audio Bitstream Scalability via Significance-Based Data Organization
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Solution Overview
Problem
Existing audio coding systems fail to maintain audio quality when compressing speech signals to low bit rates, as they do not effectively prioritize and organize data by significance, leading to deteriorated audio quality.
Innovation Solution
The method involves using a Code Excited Linear Prediction (CELP) processing module to organize audio data by significance, arranging more significant data ahead of less significant data in the bitstream, and dividing it into parts for scalable encoding and decoding, ensuring that critical information is preserved and transmitted efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If audio data is compressed to low bit rates using existing audio coding systems, then data transmission efficiency is improved, but audio quality deteriorates
Solution Approach 1:
The patent segments audio data into multiple channels (e.g., front left, front right, center, surround) and further divides each channel into significant and insignificant portions. This segmentation allows the encoder to prioritize transmission of significant data from multiple channels while discarding insignificant data, thereby maintaining audio quality at low bit rates by preserving the most important information from each channel rather than treating all data equally.
2Reliability
If more data is transmitted to maintain audio quality, then audio quality is preserved, but network bandwidth consumption increases
Solution Approach 1:
The patent applies local quality by treating different channels and different portions of channel data differently. Significant data from each channel is preserved with high quality, while insignificant data is discarded or heavily compressed. This selective quality assignment allows the system to maintain perceived audio quality by preserving critical information from multiple channels while significantly reducing the total data volume transmitted over the network.
3Adaptability or versatility
If audio data is organized by significance with more significant data placed ahead, then scalability is improved, but data processing complexity increases
Solution Approach 1:
The patent implements preliminary action by pre-organizing and pre-prioritizing audio data from multiple channels during the encoding phase. Data is sorted by significance and arranged in a predetermined scalable format before transmission. This preliminary organization enables the bitstream to be easily scaled to different bit rates at the decoder by simply truncating or selectively decoding portions of the pre-arranged data, eliminating the need for complex real-time scaling operations and reducing decoder complexity.
Data Source
AI summary
The present invention provides for methods and apparatuses for processing audio data. In one embodiment, there is provided a method for achieving bitstream scalability in a multi-channel audio encoder, said method comprising receiving audio input data; organizing said input data by a Code Excited Linear Predictor (CELP) processing module for further encoding by arranging said data according to significance of data, where more significant data is placed ahead of less significant data; and providing a scalable output bitstream; a higher bitrate bitstream is scaled to lower bitrate by discarding less significant data from frame ends. The organized CELP data comprises of a first part and a second part. The first part comprises a frame header, sub frame parameters and innovation vector quantization data from the first frame from all channels. The innovation vector quantization data from the first frames from all channels is arranged according to channel number.