{'fraction_non_alphanumeric': 0.013385826771653543, 'fraction_numerical': 0.002362204724409449, 'mean_word_length': 5.261083743842365, '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}}
An Efficient Signal-Matching Approach to Melody Indexing and Search Using Continuous Pitch Contours and Wavelets

We describe a method of indexing and efficiently searching music melodies based on their continuous dominant fundamental frequency (f0) contours without obtaining notelevel transcriptions. Each f0 contour is encoded by a redundant set of wavelet coefficients that represent its shape in level-normalized form at various locations and time scales. This allows a query melody to be exhaustively compared with variable-length portions of a target melody at arbitrary locations while accounting for differences in key and tempo. The method is applied in a Query-by-Humming (QBH) system where users may search a database of recorded pop songs by humming or singing an arbitrary part of the melody of an intended song. The system has fast retrieval times because the wavelet coefficients can be effectively indexed in a binary tree and a vector distance measure instead of dynamic programming is used for comparisons. Using automatic pitch extraction to obtain all f0 contours from acoustic data, the method demonstrates practical performance in an experiment with an existing monophonic data set and in a preliminary experiment with real-world polyphonic music.