Computational lyricology: Quantitative approaches to understanding song lyrics and their interpretations

Recently, music complexity has drawn attention from researchers in the Music Information Retrieval (MIR) area. In particular, computational methods to measure music complexity have been studied to provide better music services in large-scale music digital libraries. However, the majority of music complexity research has focused on audio-related facets of music, while song lyrics have been rarely considered. Based on the observation that most popular songs contain lyrics, whose different levels of complexity contribute to the overall music complexity, this dissertation research investigates song lyric complexity and how it might be measured computationally. In a broad sense, lyric complexity comes from two aspects of text complexity--quantitative and qualitative dimensions--that have a complementary relationship. For a comprehensive understanding of lyric complexity, this study explores both dimensions. First, for the quantitative dimensions, such as word frequency and word length, refer to those that can be measured efficiently using computer programs. Among them, this study examines the concreteness of song lyrics using trend analysis. Second, on the contrary to the quantitative dimensions, the qualitative dimensions refer to a deeper level of lyric complexity that requires attentive readers' comprehension and external knowledge. However, it is challenging to collect a large-scale qualitative analysis of lyric complexity due to the resource constraints. To this end, this dissertation introduces user-generated interpretations of song lyrics that are abundant on the web as a proxy for assessing the qualitative dimensions of lyric complexity. To be specific, this study first examines whether the user-generated data provide quality topic information, and then proposes a Lyric Topic Diversity Score (LTDS), a lyric complexity metric based on the diversity of the topics found in users' interpretations. The assumption behind this approach is that complex song lyrics tend to provoke diverse user interpretations due to their properties, such as ambiguous meanings, historical context, the author's intention, and so on. The first findings of this study include that concreteness of popular song lyrics fell from the middle of the 1960s until the 1990s and rose after that. The advent of Hip-Hop/Rap and the number of words in song lyrics are highly correlated with the rise in concreteness after the early 1990s. Second, interpretations are a good input source for automatic topic detection algorithms. Third, the interpretation-based lyric complexity metric looks promising because it is correlated with Lexical Novelty Scores (LNS), the only previously developed lyric complexity measure. Overall, this work expands the scope of music complexity by focusing on relatively unexplored data, song lyrics. Moreover, these findings suggest that any potential analysis and application on any objects can benefit from this kind of auxiliary data, which is in the form of user comments

Ten years of MIREX: reflections, challenges and opportunities

The Music Information Retrieval Evaluation eXchange (MIREX) has been run annually since 2005, with the October 2014 plenary marking its tenth iteration. By 2013, MIREX has evaluated approximately 2000 individual music information retrieval (MIR) algorithms for a wide range of tasks over 37 different test collections. MIREX has involved researchers from over 29 different contrives with a median of 109 individual participants per year. This pater summarizes the history of MIREX form its earliest planning meeting in 2001 to the present. It reflects upon the administrative, financial, and technological challenges MIREX has faced and describes how those challenges have been surmounted. We propose new funding models, a distributed evaluation framework, and more holistic user experience evaluation tasks-some evolutionary, some revolutionary-for the continued success of MIREX. We hope that this paper will inspire MIR community members to contribute their ideas so MIREX can have many more successful years to come

Applications of Bioinspired Reversible Dry and Wet Adhesives: A Review

<jats:p>Bioinspired adhesives that emulate the unique dry and wet adhesion mechanisms of living systems have been actively explored over the past two decades. Synthetic bioinspired adhesives that have recently been developed exhibit versatile smart adhesion capabilities, including controllable adhesion strength, active adhesion control, no residue remaining on the surface, and robust and reversible adhesion to diverse dry and wet surfaces. Owing to these advantages, bioinspired adhesives have been applied to various engineering domains. This review summarizes recent efforts that have been undertaken in the application of synthetic dry and wet adhesives, mainly focusing on grippers, robots, and wearable sensors. Moreover, future directions and challenges toward the next generation of bioinspired adhesives for advanced industrial applications are described.</jats:p&gt

Motor-effector dependent modulation of sensory-motor processes identified by the multivariate pattern analysis of EEG activity

Sensory information received through sensory organs is constantly modulated by numerous non-sensory factors. Recent studies have demonstrated that the state of action can modulate sensory representations in cortical areas. Similarly, sensory information can be modulated by the type of action used to report perception; however, systematic investigation of this issue is scarce. In this study, we examined whether sensorimotor processes represented in electroencephalography (EEG) activities vary depending on the type of effector behavior. Nineteen participants performed motion direction discrimination tasks in which visual inputs were the same, and only the effector behaviors for reporting perceived motion directions were different (smooth pursuit, saccadic eye movement, or button press). We used multivariate pattern analysis to compare the EEG activities for identical sensory inputs under different effector behaviors. The EEG activity patterns for the identical sensory stimulus before any motor action varied across the effector behavior conditions, and the choice of motor effectors modulated the neural direction discrimination differently. We suggest that the motor-effector dependent modulation of EEG direction discrimination might be caused by effector-specific motor planning or preparation signals because it did not have functional relevance to behavioral direction discriminability. © 2023, The Author(s).11Nsciescopu

Active Motif Change of Ni-Fe Spinel Oxide by Ir Doping for Highly Durable and Facile Oxygen Evolution Reaction

© 2022 Wiley-VCH GmbH.The oxygen evolution reaction (OER) is crucial for producing sustainable energy carriers. Herein, Ir (5 mol.%) doped inverse-spinel NiFe2O4 (Ir-NFO) nanoparticles deposited on Ni foam (NF) by scalable solution casting are considered a promising OER electrocatalyst for industrial deployments. The Ir-NFO/NF (with minimal lattice distortion by uniform Ir doping) provides an OER overpotential of 251 mV (intrinsically outperforming NFO/NF and benchmarking IrO2/NF) and extraordinary robustness over 130 days at 100 mA cm−2. In situ X-ray absorption spectroscopy reveals oxidation only for Fe on NFO, whereas concurrent generation of higher-valent Ni and Fe occurs on Ir-NFO during OER. Density functional theory calculations further demonstrate that Ir substitutes the sublayer Ni octahedral site and switches the main active reaction center from FeOhFeTd bridge site (FeOFe) on NFO to NiOh–FeTd bridge site (NiOFe active motif) on Ir-NFO for a co-catalytic OER. This study sheds new light on precious-metal doped Ni-Fe oxides, which may be applicable to other binary/ternary oxide electrocatalysts.11Nsciescopu