Improving Note Segmentation in Automatic Piano Music Transcription Systems with a Two-State Pitch-Wise HMM Method

Many methods for automatic piano music transcription involve a multi-pitch estimation method that estimates an activity score for each pitch. A second processing step, called note segmentation, has to be performed for each pitch in order to identify the time intervals when the notes are played. In this study, a pitch-wise two-state on/off first-order Hidden Markov Model (HMM) is developed for note segmentation. A complete parametrization of the HMM sigmoid function is proposed, based on its original regression formulation, including a parameter α of slope smoothing and β of thresholding contrast. A comparative evaluation of different note segmentation strategies was performed, differentiated according to whether they use a fixed threshold, called “Hard Thresholding” (HT), or a HMM-based thresholding method, called “Soft Thresholding” (ST). This evaluation was done following MIREX standards and using the MAPS dataset. Also, different transcription and recording scenarios were tested using three units of the Audio Degradation toolbox. Results show that note segmentation through a HMM soft thresholding with a data-based optimization of the {α, β} parameter couple significantly enhances transcription performance.