{'fraction_non_alphanumeric': 0.018907563025210083, 'fraction_numerical': 0.0, 'mean_word_length': 5.555045871559633, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Integration of a Priori and Estimated Constraints Into an MVDR Beamformer for Speech Enhancement

Conventionally, the single constraint of the minimum variance distortionless response (MVDR) beamformer for speech enhancement has been defined using one of two approaches. Either it is based on a priori assumptions such as microphone characteristics, position, speech source location, and room acoustics, or on a relative transfer function (RTF) vector estimate using a data dependent method. Each approach has its respective merits and drawbacks and a decision usually has to be made between one of the approaches. In this paper, an alternative approach of using an integrated MVDR beamformer is investigated, where both the hard constraints from the two conventional approaches are softened to yield two tuning parameters. It will be shown that this integrated MVDR beamformer can be expressed as a convex combination of the conventional MVDR beamformers, a linearly constrained minimum variance (LCMV) beamformer, and an all-zero vector, with real, positive-valued coefficients. By analysing how the tuning parameters affect these coefficients, two tuning rules for a practical implementation of the integrated MVDR are subsequently proposed. An evaluation with simulated and recorded data demonstrates that the integrated MVDR beamformer can be beneficial as opposed to relying on either of the conventional MVDR beamformers.