{'fraction_non_alphanumeric': 0.013052208835341365, 'fraction_numerical': 0.0, 'mean_word_length': 5.350318471337579, '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}}
Unsupervised singing voice detection using dictionary learning

This paper presents an unsupervised approach to vocal detection in music recordings based on dictionary learning. At a first stage, the recording to be segmented is treated as training data and the K-SVD algorithm is used to learn a dictionary which sparsely represents a short-term feature sequence that has been extracted from the recording. Subsequently, the vectors of the feature sequence are reconstructed based on the learned dictionary and the probability of appearance of the dictionary atoms is estimated. The obtained probability serves to compute the value of a weight function for each frame of the recording. The histogram of this function is then used to estimate a binarization threshold that segments the recording into vocal and non-vocal segments. The performance of the proposed unsupervised method, when evaluated on two datasets of accompanied singing, presents comparable performance to supervised techniques.