Statistical Piano Reduction Controlling Performance Difficulty

We present a statistical-modelling method for piano reduction, i.e. converting an ensemble score into piano scores, that can control performance difficulty. While previous studies have focused on describing the condition for playable piano scores, it depends on player's skill and can change continuously with the tempo. We thus computationally quantify performance difficulty as well as musical fidelity to the original score, and formulate the problem as optimization of musical fidelity under constraints on difficulty values. First, performance difficulty measures are developed by means of probabilistic generative models for piano scores and the relation to the rate of performance errors is studied. Second, to describe musical fidelity, we construct a probabilistic model integrating a prior piano-score model and a model representing how ensemble scores are likely to be edited. An iterative optimization algorithm for piano reduction is developed based on statistical inference of the model. We confirm the effect of the iterative procedure; we find that subjective difficulty and musical fidelity monotonically increase with controlled difficulty values; and we show that incorporating sequential dependence of pitches and fingering motion in the piano-score model improves the quality of reduction scores in high-difficulty cases.Comment: 12 pages, 7 figures, version accepted to APSIPA Transactions on Signal and Information Processin

Tracking the Evolution of a Band's Live Performances over Decades

International Society for Music Information Retrieval Conference (ISMIR 2022) , Bengaluru, India, December 4-8, 2022Evolutionary studies have become a dominant thread in the analysis of large audio collections. Such corpora usually consist of musical pieces by various composers or bands and the studies usually focus on identifying general historical trends in harmonic content or music production techniques. In this paper we present a comparable study that examines the music of a single band whose publicly available live recordings span three decades. We first discuss the opportunities and challenges faced when working with single-artist and live-music datasets and introduce solutions for audio feature validation and outlier detection. We then investigate how individual songs vary over time and identify general performance trends using a new approach based on relative feature values, which improves accuracy for features with a large variance. Finally, we validate our findings by juxtaposing them with descriptions posted in online forums by experienced listeners of the band's large following

End-to-End Lyrics Transcription Informed by Pitch and Onset Estimation

International Society for Music Information Retrieval Conference (ISMIR 2022) , Bengaluru, India, December 4-8, 2022This paper presents an automatic lyrics transcription (ALT) method for music recordings that leverages the framewise semitone-level sung pitches estimated in a multi-task learning framework. Compared to automatic speech recognition (ASR), ALT is challenging due to the insufficiency of training data and the variation and contamination of acoustic features caused by singing expressions and accompaniment sounds. The domain adaptation approach has thus recently been taken for updating an ASR model pre-trained from sufficient speech data. In the naive application of the end-to-end approach to ALT, the internal audio-to-lyrics alignment often fails due to the time-stretching nature of singing features. To stabilize the alignment, we make use of the semi-synchronous relationships between notes and characters. Specifically, a convolutional recurrent neural network (CRNN) is used for estimating the semitone-level pitches with note onset times while eliminating the intra- and inter-note pitch variations. This estimate helps an end-to-end ALT model based on connectionist temporal classification (CTC) learn correct audio-to-character alignment and mapping, where the ALT model is trained jointly with the pitch and onset estimation model. The experimental results show the usefulness of the pitch and onset information in ALT

Interaction between Calcineurin and Ca2+/Calmodulin Kinase-II in Modulating Cellular Functions

Roles of calcineurin (CaN), a Ca2+/calmodulin- (CaM-) dependent protein phosphatase, and Ca2+/CaM-dependent protein kinase-II (CaMKII) in modulating K+ channel activity and the intracellular Ca2+ concentration ([Ca2+]i) have been investigated in renal tubule epithelial cells. The channel current through the cell membrane was recorded with the patch-clamp technique, and [Ca2+]i was monitored using fura-2 imaging. We found that a CaN-inhibitor, cyclosporin A (CyA), lowered the K+ channel activity and elevated [Ca2+]i, suggesting that CyA closes K+ channels and opens Ca2+-release channels of the cytosolic Ca2+-store. Moreover, both of these responses were blocked by KN-62, an inhibitor of CaMKII. It is suggested that the CyA-mediated response results from the activation of CaMKII. Indeed, Western blot analysis revealed that CyA increased phospho-CaMKII, an active form of CaMKII. These findings suggest that CaN-dependent dephosphorylation inhibits CaMKII-mediated phosphorylation, and the inhibition of CaN increases phospho-CaMKII, which results in the stimulation of CaMKII-dependent cellular actions

Rhythm Transcription of Polyphonic MIDI Performances Based on a Merged-output HMM for Multiple Voices

(Abstract to follow

Features of the Atrophic Corpus Mucosa in Three Cases of Autoimmune Gastritis Revealed by Magnifying Endoscopy

Atrophic gastritis, whether caused by Helicobacter pylori infection or as a result of an autoimmune process, is associated with corpus atrophy. However, whereas atrophic gastritis caused by H. pylori involves the antrum, the antrum is spared in autoimmune gastritis. Here, we report the use of magnifying endoscopy to identify and distinguish atrophic gastritis caused by H. pylori from autoimmune gastritis. The mucosal pattern in autoimmune gastritis is that of closely arranged small round and oval pits, thus differing from the pattern seen in atrophic mucosa due to H. pylori infection. We speculate that this reflects differences in inflammation between the two types of gastritis. In autoimmune gastritis the inflammation is directed primarily against gastric glands, whereas in H. pylori infection the inflammation is directed against the bacteria on or near the surface and the damage initially affects the surface epithelium. During repair, the normal regular round pits are destroyed, whereas they remain largely intact in mucosa with autoimmune-associated atrophy. Confirmation of the features of autoimmune gastritis revealed by magnifying endoscopy would not only make the endoscopic diagnosis of autoimmune gastritis more accurate, but also help to elucidate changes in the surface epithelial structure of gastritis due to various causes