{'fraction_non_alphanumeric': 0.010282776349614395, 'fraction_numerical': 0.005141388174807198, 'mean_word_length': 5.182539682539683, '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}}
Spectral subtraction and spectral estimation

The problem of spectral subtraction, to estimate the parameters of a single source in colored noise, is used to show the relationships between the likelihood formulation and spectral density estimation. Reported previously as a filter bank processing for spectral estimation, it is shown that the normalized Capon estimate is the natural tool for source location in 1D and 2D scenarios when the noise background estimate is faced as a spectral subtraction problem. Several simulations selecting 1D and 2D apertures are used to show the degree of quality achieved with the proposed formulation. Also, the periodogram test for incoherent detection is analyzed in front of the optimum test and the herein referred to as the Capon test.