Titlebook: Artificial Intelligence in Music, Sound, Art and Design; 13th International C Colin Johnson,Sérgio M. Rebelo,Iria Santos Conference proceed

minutia

書目名稱Artificial Intelligence in Music, Sound, Art and Design影響因子(影響力)




書目名稱Artificial Intelligence in Music, Sound, Art and Design影響因子(影響力)學(xué)科排名




書目名稱Artificial Intelligence in Music, Sound, Art and Design網(wǎng)絡(luò)公開度




書目名稱Artificial Intelligence in Music, Sound, Art and Design網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Artificial Intelligence in Music, Sound, Art and Design被引頻次




書目名稱Artificial Intelligence in Music, Sound, Art and Design被引頻次學(xué)科排名




書目名稱Artificial Intelligence in Music, Sound, Art and Design年度引用




書目名稱Artificial Intelligence in Music, Sound, Art and Design年度引用學(xué)科排名




書目名稱Artificial Intelligence in Music, Sound, Art and Design讀者反饋




書目名稱Artificial Intelligence in Music, Sound, Art and Design讀者反饋學(xué)科排名

Missile

Efflorescent

,Deep Learning Approaches for?Sung Vowel Classification,dels, we find that a fine-tuned transformer performed the strongest; however, a convolutional or recurrent model may provide satisfactory results in resource-limited scenarios. This result implies that neural approaches trained directly on raw audio, without extracting spectral features, are viable

起來了

,Weighted Initialisation of?Evolutionary Instrument and?Pitch Detection in?Polyphonic Music,ds to create false positives which may conceal the true potential of our modified approach. Regardless of that, our modification still shows significantly faster convergence speed and slightly improved pitch and instrument detection errors over the baseline algorithm on both single onset and full pi

蝕刻術(shù)

,Modelling Individual Aesthetic Preferences of?3D Sculptures, model is flexible enough to identify and respond to individual aesthetic preferences, handling the subjectivity at the root of aesthetic preference and providing a good base for further extension to strengthen the ability of the system to model individual aesthetic preference.

foodstuff

,Adaptation and?Optimization of?AugmentedNet for?Roman Numeral Analysis Applied to?Audio Signals,ations and has shown that some of the optimization steps significantly increased the classification performance. We find that this adapted AugmentedNet can reach similar accuracy levels when faced with audio features as it achieves with the “cleaner” symbolic data on which it was originally trained.

Carcinogenesis

,Generating Smooth Mood-Dynamic Playlists with?Audio Features and?KNN,ions and the variance of steps between songs, respectively. Our algorithm successfully creates smooth and evenly-spaced playlists that transition cohesively in both mood and genre. We explore how the choice of audio feature data, similarity metric, and KNN parameters all have an effect on playlists’

Noisome

慷慨不好

,Towards Sound Innovation Engines Using Pattern-Producing Networks and?Audio Graphs,ombination of Compositional Pattern Producing Network (CPPN) + Digital Signal Processing (DSP) graphs coupled with Multi-dimensional Archive of Phenotypic Elites (MAP-Elites) and a deep learning classifier can generate a substantial variety of synthetic sounds. The study concludes by presenting the

Endometrium

,Co-creative Orchestration of?, with?Layer Scores and?Orchestration Plans,hrough instrumentation presets, and finally through selection of the final orchestral plan and through the actual orchestration. We detail the research aspects of this co-creative project and analyze the roles of the actors involved in the creation of the final piece: the Music Information Retrieval