AccoMontage-3: Full-Band Accompaniment Arrangement via Sequential Style Transfer and Multi-Track Function Prior

We propose AccoMontage-3, a symbolic music automation system capable of generating multi-track, full-band accompaniment based on the input of a lead melody with chords (i.e., a lead sheet). The system contains three modular components, each modelling a vital aspect of full-band composition. The first component is a piano arranger that generates piano accompaniment for the lead sheet by transferring texture styles to the chords using latent chord-texture disentanglement and heuristic retrieval of texture donors. The second component orchestrates the piano accompaniment score into full-band arrangement according to the orchestration style encoded by individual track functions. The third component, which connects the previous two, is a prior model characterizing the global structure of orchestration style over the whole piece of music. From end to end, the system learns to generate full-band accompaniment in a self-supervised fashion, applying style transfer at two levels of polyphonic composition: texture and orchestration. Experiments show that our system outperforms the baselines significantly, and the modular design offers effective controls in a musically meaningful way

Über die kritischen Werte der Rankin-Selberg-Faltungen zu nicht-cuspidalen Gelbart-Jacquet-Lifts

Gegenstand dieser Arbeit ist die Rankin-Selberg-L-Funktion zu einer elliptischen Kurve mit komplexer Multiplikation und deren symmetrischem Quadrat. Wir stellen sie dar als Produkt der Hecke-L-Funktionen zu Größencharakteren, bestimmen die Funktionalgleichung und untersuchen den Funktionswert an der kritischen Stelle auf Algebraizität. Im Anschluss befassen wir uns mit der p-adischen Interpolation der kritischen Werte und vergleichen zwei verschiedene Interpolationsmethoden

Image-based retrieval of all-day cloud physical parameters for FY4A/AGRI and its application over the Tibetan Plateau

Satellite remote sensing serves as a crucial means to acquire cloud physical parameters. However, existing official cloud products derived from the advanced geostationary radiation imager (AGRI) onboard the Fengyun-4A geostationary satellite suffer from limitations in computational precision and efficiency. In this study, an image-based transfer learning model (ITLM) was developed to realize all-day and high-precision retrieval of cloud physical parameters using AGRI thermal infrared measurements and auxiliary data. Combining the observation advantages of geostationary and polar-orbiting satellites, ITLM was pre-trained and transfer-trained with official cloud products from advanced Himawari imager (AHI) and Moderate Resolution Imaging Spectroradiometer (MODIS), respectively. Taking official MODIS products as the benchmarks, ITLM achieved an overall accuracy of 79.93% for identifying cloud phase and root mean squared errors of 1.85 km, 6.72 um, and 12.79 for estimating cloud top height, cloud effective radius, and cloud optical thickness, outperforming the precision of official AGRI and AHI products. Compared to the pixel-based random forest model, ITLM utilized the spatial information of clouds to significantly improve the retrieval performance and achieve more than a 6-fold increase in speed for a single full-disk retrieval. Moreover, the AGRI ITLM products with spatiotemporal continuity and high precision were used to accurately describe the spatial distribution characteristics of cloud fractions and cloud properties over the Tibetan Plateau (TP) during both daytime and nighttime, and for the first time provide insights into the diurnal variation of cloud cover and cloud properties for total clouds and deep convective clouds across different seasons

Polyffusion: A Diffusion Model for Polyphonic Score Generation with Internal and External Controls

We propose Polyffusion, a diffusion model that generates polyphonic music scores by regarding music as image-like piano roll representations. The model is capable of controllable music generation with two paradigms: internal control and external control. Internal control refers to the process in which users pre-define a part of the music and then let the model infill the rest, similar to the task of masked music generation (or music inpainting). External control conditions the model with external yet related information, such as chord, texture, or other features, via the cross-attention mechanism. We show that by using internal and external controls, Polyffusion unifies a wide range of music creation tasks, including melody generation given accompaniment, accompaniment generation given melody, arbitrary music segment inpainting, and music arrangement given chords or textures. Experimental results show that our model significantly outperforms existing Transformer and sampling-based baselines, and using pre-trained disentangled representations as external conditions yields more effective controls.Comment: In Proceedings of the 24th Conference of the International Society for Music Information Retrieval (ISMIR 2023), Milan, Ital