Prioritizing signals for selective real-time audio processing
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This paper studies various priority metrics that can be used to progressively select sub-parts of a number of audio signals for realtime processing. In particular, five level-related metrics were examined: RMS level, A-weighted level, Zwicker and Moore loudness models and a masking threshold-based model. We conducted a pilot subjective evaluation study aimed at evaluating which metric would perform best at reconstructing mixtures of various types (speech, ambient and music) using only a budget amount of original audio data. Our results suggest that A-weighting performs the worst while results obtained with loudness metrics appear to depend on the type of signals. RMS level offers a good compromise for all cases. Our results also show that significant sub-parts of the original audio data can be omitted in most cases, without noticeable degradation in the generated mixtures, which validates the usability of our selective processing approach for real-time applications. In this context, we successfully implemented a prototype 3D audio rendering pipeline using our selective approach.