Research On Polyphonic Music Pitch Estimation

Multi-Pitch Estimation (MPE), or say Multiple Fundamental Frequency (F0) Es-timation, is one of the most important and di?cult issues in the area of Music Informa-tion Retrieval (MIR). Its main tasks are to estimate the pitches (F0s) and their number(polyphony) at any time. Sometimes the note onsets and o?sets are also should beestimated.This thesis starts from introducing the background of the MIR research, thenpresents the main tasks, research values and relations to other research. After that,it systematically reviews some typical pitch estimation algorithms. Finally, it proposestwo new algorithms.The first algorithm is a signle-frame MPE algorithm based on maximum likeli-hood spectral modeling. Di?erent from the traditional whole spectral modeling meth-ods, this algorithm reduces the frequency spectrum into peaks and non-peak areas inthe amplitude spectrum, and the peaks are further reduced into their frequencies andamplitudes. Along with the reductions, the maximum likelihood model is split into twoparts: the peak likelihood and the non-peak area likelihood. In modeling the peaks, theconcepts of"true"and"false"peaks are proposed and modeled separately, to cope withthe errors in the peak detection method. In modeling the non-peak area likelihood, theprobability that the peaks which are generated by the harmonics but not detected is setto the likelihood function. The two parts of the likelihood function models di?erentaspects and are complementary. Their parameters are learned from monophonic train-ing data, where the"true"and"false"peaks are easy to be discriminated. A weightedBayesian Information Criteria (BIC) is employed to estimate the polyphony. Finally,the algorithm is tested on random chords and musical chords, which are both generatedusing the real instrumental notes. The experimental results are promising.The second algorithm is a multiple-frame MPE algorithm based on ComputationalAuditory Scene Analysis (CASA). In this algorithm, we simulate the auditory cues of human perception, to group the time-frequency components. More concretely, the con-cept of partial event is defined. Each partial event is a four-element vector (frequency,amplitude, onset and o?set). For a piece of music to be processed, all its partial eventsare extracted to compose a set, in which each event is a candidate of the F0 event. Thena support transfer algorithm is designed to make the events vote to each other, to electthe ones with highest degrees to be F0s. The proposed algorithm is tested on randomchords which are generated using real instrumental notes, and on computer-synthesizedchamber music. The results are promising.