Perceptual Evaluation of Blind Source Separation in Object-Based Audio Production

Object-based audio has the potential to enable multimedia content to be tailored to individual listeners and their reproduction equipment. In general, object-based production assumes that the objects—the assets comprising the scene—are free of noise and interference. However, there are many applications in which signal separation could be useful to an object-based audio workflow, e.g., extracting individual objects from channel-based recordings or legacy content, or recording a sound scene with a single microphone array. This paper describes the application and evaluation of blind source separation (BSS) for sound recording in a hybrid channel-based and object-based workflow, in which BSS-estimated objects are mixed with the original stereo recording. A subjective experiment was conducted using simultaneously spoken speech recorded with omnidirectional microphones in a reverberant room. Listeners mixed a BSS-extracted speech object into the scene to make the quieter talker clearer, while retaining acceptable audio quality, compared to the raw stereo recording. Objective evaluations show that the relative short-term objective intelligibility and speech quality scores increase using BSS. Further objective evaluations are used to discuss the influence of the BSS method on the remixing scenario; the scenario shown by human listeners to be useful in object-based audio is the worst-case scenario among those tested.