This work explores the use of source-filter models for pitch/nsalience estimation and their combination with different/npitch tracking and voicing estimation methods for automatic/nmelody extraction. Source-filter models are used/nto create a mid-level representation of pitch that implicitly/nincorporates timbre information. The spectrogram of/na musical audio signal is modelled as the sum of the leading/nvoice (produced by human voice or pitched musical instruments)/nand accompaniment. The leading ...
This work explores the use of source-filter models for pitch/nsalience estimation and their combination with different/npitch tracking and voicing estimation methods for automatic/nmelody extraction. Source-filter models are used/nto create a mid-level representation of pitch that implicitly/nincorporates timbre information. The spectrogram of/na musical audio signal is modelled as the sum of the leading/nvoice (produced by human voice or pitched musical instruments)/nand accompaniment. The leading voice is then/nmodelled with a Smoothed Instantaneous Mixture Model/n(SIMM) based on a source-filter model. The main advantage/nof such a pitch salience function is that it enhances/nthe leading voice even without explicitly separating it from/nthe rest of the signal. We show that this is beneficial/nfor melody extraction, increasing pitch estimation accuracy/nand reducing octave errors in comparison with simpler/npitch salience functions. The adequate combination with/nvoicing detection techniques based on pitch contour characterisation/nleads to significant improvements over stateof-the-art/nmethods, for both vocal and instrumental music.
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