Robust and quality-assured audio-visual communication

失真的听觉感知建模在当今压缩语音和音频的广泛使用中起着至关重要的作用。通过移动电话进行的每一次语音交谈或MP3音乐的下载都需要使用基于感知模型的失真测量。当语音或音频信号被压缩成数字格式时，失真模型被用来确定在信号中的什么位置来“隐藏”失真，以使它们最不容易被察觉。我们建议使用新一代感知模型进行研究，以构建更强大的工具，用于在新兴和未来的网络配置上进行语音通信和音频分发。其中一个目标是构建机器智能，使计算机能够以与人类收听者小组相同的方式对语音信号的质量进行评级。这将使通信网元和电子设备能够根据人类感知的语音质量更好地优化资源使用。另一个目标是设计新的数字格式化算法，使音频能够有效和可靠地分发到非常大量的客户。格式将非常灵活，因此，考虑到客户端网络连接的特定质量，每个客户端的用户感知的音频质量是最好的。预期的一个好处是，高保真音频网络广播可以接触到更多的用户。

Auditory perceptual modeling of distortions plays a vital role in enabling today's widespread utilization of compressed speech and audio. Every voice conversation over a cellular telephone or download of MP3 music entails the use of perceptual-model based measurement of distortions.  When a speech or audio signal is compressed into a digital format, a distortion model is used to determine where in the signal to "hide" distortions to make them least perceptible. We propose to research using a new generation of perceptual models to build more powerful tools for speech communication and audio distribution over emerging and future network configurations.   One goal is to build machine intelligence to enable computers to rate the quality of a speech signal in the same manner as a panel of human listeners.  This would enable communication network elements and electronic devices to better optimize resource usage in accordance with human-perceived speech quality. Another goal is to devise new digital formatting algorithms that would enable efficient and reliable distribution of audio to a very large number of clients. The format will be very flexible so that the user-perceived audio quality at each client is the best possible given the particular quality of the client's network connection.  One anticipated benefit is that high-fidelity audio net-casting can reach a much greater number of users.