Whether literally or suggestively, the concept of soundscape is alluded in both modern and ancient music. In this study, we examine whether we can analyze and compare Western and Chinese classical music based on soundscape models. We addressed this question through a comparative study. Specifically, corpora of Western classical music excerpts (WCMED) and Chinese classical music excerpts (CCMED) were curated and annotated with emotional valence and arousal through a crowdsourcing experiment. We used a sound event detection (SED) and soundscape emotion recognition (SER) models with transfer learning to predict the perceived emotion of WCMED and CCMED. The results show that both SER and SED models could be used to analyze Chinese and Western classical music. The fact that SER and SED work better on Chinese classical music emotion recognition provides evidence that certain similarities exist between Chinese classical music and soundscape recordings, which permits transferability between machine learning models.
From West to East: Who can understand the music of the others better?
National Technical University of Athens（雅典国立技术大学）；School of ECE, National Technical University of Athens（雅典国立技术大学电子与计算机工程学院）；Queen Mary University of London（伦敦大学玛丽皇后学院）；Centre for Digital Music, Queen Mary University of London（伦敦大学玛丽皇后学院数字音乐中心）
Supervised deep learning methods for performing audio source separation can be very effective in domains where there is a large amount of training data. While some music domains have enough data suitable for training a separation system, such as rock and pop genres, many musical domains do not, such as classical music, choral music, and non-Western music traditions. It is well known that transferring learning from related domains can result in a performance boost for deep learning systems, but it is not always clear how best to do pretraining. In this work we investigate the effectiveness of d…
Every art form ultimately aims to invoke an emotional response over the audience, and music is no different. While the precise perception of music is a highly subjective topic, there is an agreement in the "feeling" of a piece of music in broad terms. Based on this observation, in this study, we aimed to determine the emotional feeling associated with short passages of music; specifically by analyzing the melodic aspects. We have used the dataset put together by Eerola et. al. which is comprised of labeled short passages of film music. Our initial survey of the dataset indicated that other tha…
The relationship between sensory consonance and Western harmony is an important topic in music theory and psychology. We introduce new methods for analysing this relationship, and apply them to large corpora representing three prominent genres of Western music: classical, popular, and jazz music. These methods centre on a generative sequence model with an exponential-family energy-based form that predicts chord sequences from continuous features. We use this model to investigate one aspect of instantaneous consonance (harmonicity) and two aspects of sequential consonance (spectral distance and…
Universidad Rey Juan Carlos（胡安·卡洛斯国王大学）；Universidad Carlos III de Madrid（马德里卡洛斯三世大学）
In the last decade, soundscapes have become one of the most active topics in Acoustics, providing a holistic approach to the acoustic environment, which involves human perception and context. Soundscapes-elicited emotions are central and substantially subtle and unnoticed (compared to speech or music). Currently, soundscape emotion recognition is a very active topic in the literature. We provide an exhaustive variable selection study (i.e., a selection of the soundscapes indicators) to a well-known dataset (emo-soundscapes). We consider linear soundscape emotion models for two soundscapes desc…
Artificial Intelligence Research Institute, Spanish National Research Council (IIIA-CSIC)（西班牙国家研究委员会人工智能研究所（IIIA-CSIC））；Centre de Recerca Matemàtica（数学研究中心）；Universitat de Barcelona（巴塞罗那大学）；Universitat Pompeu Fabra（庞培法布拉大学）
Popular music is a key cultural expression that has captured listeners' attention for ages. Many of the structural regularities underlying musical discourse are yet to be discovered and, accordingly, their historical evolution remains formally unknown. Here we unveil a number of patterns and metrics characterizing the generic usage of primary musical facets such as pitch, timbre, and loudness in contemporary western popular music. Many of these patterns and metrics have been consistently stable for a period of more than fifty years, thus pointing towards a great degree of conventionalism. None…
In the previous articles of this series, we have discussed the development of musical scales particularly that of the heptatonic scale which forms the basis of Western classical music today. In this last article, we take a look at the basic structure of scales used in Indian classical music and how different `raga's are generated through the simple process of scale shifting.
MusicMamba: A Dual-Feature Modeling Approach for Generating Chinese Traditional Music with Modal Precision
School of Computer Science and Artificial Intelligence, Wuhan University of Technology（武汉理工大学计算机科学与人工智能学院）；School of Computer Science, Hubei University of Technology（湖北工业大学计算机学院）
Are Expressions for Music Emotions the Same Across Cultures?
Max Planck Institute for Empirical Aesthetics（马克斯·普朗克实证美学研究所）；Max Planck Institute for Human Cognitive and Brain Sciences（马克斯·普朗克人类认知与脑科学研究所）；Cornell University（康奈尔大学）
Music Genre Classification: A Comparative Analysis of Classical Machine Learning and Deep Learning Approaches
Computer Science and Engineering（计算机科学与工程）；University of Texas at Arlington（德克萨斯大学阿灵顿分校）；Tribhuvan University, IOE（特里布文大学理工学院）；Arlington, USA（美国阿灵顿）；kathmandu（加德满都）；Dharan（达尔汉）
Local Periodicity-Based Beat Tracking for Expressive Classical Piano Music
National Cheng Kung University（国立成功大学）；Academia Sinica（中央研究院）；International Audio Laboratories Erlangen（埃尔朗根国际音频实验室）；Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；Fraunhofer Institute for Integrated Circuits IIS（弗劳恩霍夫集成电路研究所）；University of Coimbra（科英布拉大学）；Taiwan AI Labs（台湾人工智能实验室）
