Pre-Echo Reduction in Transform Audio Coding via Temporal Envelope Control with Machine Learning Based Estimation

This paper proposes a new method for pre-echo reduction in transform-based audio coding by controlling the temporal envelope of the waveform. The proposed method comprises two operating modes: temporal envelope flattening and temporal envelope correction of a target signal. The proposed method estimates signal levels with a low temporal resolution from side information using machine learning and converts them into a signal to be applied to the target signal to flatten and correct the temporal envelope. It also adjusts the signals to maintain signal continuity between the non-transient and transient frames. The proposed method differs from conventional methods in that it directly modifies the waveform before encoding and after decoding, which makes it useful as a new coding tool for legacy codecs. A subjective performance evaluation confirms that the proposed method uses fewer bits to provide sound quality equivalent to that of the short-window transform.
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