Guest editorial: Music perception and cognition in music technology

Abstract

There has been a remarkably increasing interest in music technology in the past few years, which is a multi-disciplinary overlapping research area. It involves digital signal processing, acoustics, mechanics, computer science, electronic engineering, artificial intelligence psychophysiology, cognitive neuroscience and music performance, theory and analysis. Among these sub-domains of music technology, Music Perception and Cognition are important parts of Computational Musicology as Musiking is a whole activity from music noumenon to being perceived and cognised by human beings. In addition to the calculation of basic elements of music itself, such as rhythm, pitch, timbre, harmony and structure, the perception of music to the human ear and the creative cognitive process should gain more attention from researchers because it serves as a bridge to join the humanity and technology. Music perception exists in almost every aspect related to music, such as composing, playing, improvising, performing, teaching and learning. It is so comprehensive that a range of disciplines, including cognitive musicology, musical timbre perception, music emotions, acoustics, audio-based music signal processing, music interactive, cognitive modelling and music information retrieval, can be incorporated. This special issue aims to bring together humanity and technology scientists in music technology in areas such as music performance art, creativity, computer science, experimental psychology, and cognitive science. It is composed of 10 outstanding contributions covering auditory attention selection behaviours, emotional music generation, instrument and performance skills recognition, perception and musical elements, music educational robots, affective computing, music-related social behaviour, and cross-cultural music dataset. Li et al. studied the automatic recognition of traditional Chinese musical instrument audio. Specifically in the instrument type identification experiment, Mel-spectrum is used as input, and an 8-layer convolutional neural network is trained. This configuration achieves 99.3% accuracy; in the performance skills recognition experiments respectively conducted on single-instrument level and same-kind instruments level where the regularity of the same playing technique of different instruments can be utilised. The recognition accuracy of the four kinds of instruments is as follows: 95.7% for blowing instruments, 82.2% for plucked string instruments, 88.3% for strings instruments, and 97.5% for percussion instruments with a similar training procedure configuration. Wang et al. used a cross-cultural approach to explore the correlations between perception and musical elements by comparing music emotion recognition models. In this approach, the participants are asked to rate valence, tension arousal and energy arousal on labelled nine-point analogical-categorical scales for four types of classical music: Chinese ensemble, Chinese solo, Western ensemble and Western solo. Fifteen musical elements in five categories—timbre, rhythm, articulation, dynamics and register were annotated through manual evaluation or the automatic algorithm. Results showed that tempo, rhythm complexity, and articulation are culturally universal, but musical elements related to timbre, register and dynamic features are culturally specific. Du et al. proposed a multi-scale ASA model based on the binary Logit model by referencing the information value and saliency-driven factors of the listener's attention behaviour. The experiment for verification showed that the proposed ASA model was an effectively predicted human selective auditory attention feature. The improvement of the proposed ASA model with auditory attention research studies and traditional attention models is embodied in cognitive specialties that coincide more with the authentic auditory attention process and its application in the practical HMS optimisation. Furthermore, by adopting the proposed ASA mo