The analysis of mood taxonomy comparision between chinese and western music

Zhao, Zhimin; Xie, Lingyun; Liu, Jing; Wu, Wen

doi:10.1109/icsps.2010.5555502

Cited by 6 publications

(6 citation statements)

References 9 publications

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“…It can be seen from Table 2 that timbre features are the most commonly used in MER models. This is due to the fact that they have shown to provide the best performance in MER systems when used as individual features [66] [93]. Indeed, Schmidt et al investigated the use of multiple audio content-based features both individually and in combination in a feature fusion system [66] [63].…”

Section: Content and Context-based Featuresmentioning

confidence: 99%

“…The best individual features were octave-based spectral contrast and MFCCs. However, the best overall results were achieved using a combination of features, as in [93] (combination of rhythm, timbre and pitch features). Eerola et al [18] extracted features representing six di↵erent musical variables (dynamics, timbre, harmony, register, rhythm and articulation) to further apply statistical feature selection (FS) methods: multiple linear regression (MLR) with a stepwise FS principle, principle component analysis (PCA) followed by the selection of an optimal number of components, and partial least square regression (PLSR) with a Bayesian information criterion (BIC) to select the optimal number of features.…”

Section: Content and Context-based Featuresmentioning

confidence: 99%

See 1 more Smart Citation

Music Emotion Recognition: From Content- to Context-Based Models

Barthet

Fazekas

Sandler

2013

From Sounds to Music and Emotions

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The striking ability of music to elicit emotions assures its prominent status in human culture and every day life. Music is often enjoyed and sought for its ability to induce or convey emotions, which may manifest in anything from a slight variation in mood, to changes in our physical condition and actions. Consequently, research on how we might associate musical pieces with emotions and, more generally, how music brings about an emotional response is attracting ever increasing attention. First, this paper provides a thorough review of studies on the relation of music and emotions from di↵erent disciplines. We then propose new insights to enhance automated music emotion recognition models using recent results from psychology, musicology, a↵ective computing, semantic technologies and music information retrieval.

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Section: Content and Context-based Featuresmentioning

confidence: 99%

Section: Content and Context-based Featuresmentioning

confidence: 99%

Music Emotion Recognition: From Content- to Context-Based Models

Barthet

Fazekas

Sandler

2013

From Sounds to Music and Emotions

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“…The spectral centroid measures the "center of gravity" of the sound spectrum, and it is strongly correlated with the brightness of a sound as the "balancing point" of the spectrum [18]. ¦ ¦ (4) where X[k] is the magnitude corresponding to bin k and N is the length of the FFT.…”

Section: B Perceptual Featuresmentioning

confidence: 99%

“…In [3], the music segments as set as 30 seconds period. Music chips of 16 kHz, 16 bit, and mono-channel with 30 seconds length were evaluated in [4], concerning both Chinese traditional music and western classic music. In some of the other work [5] [6], the segment duration of music clips were not emphasized.…”

Section: Introductionmentioning

confidence: 99%

Music mood tracking based on HCS

Xiao

Zhang

et al. 2012

2012 IEEE 11th International Conference on Signal Processing

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Music mood present the inherent emotional state of music on certain duration of music segment. The mood may vary in an entire piece of music, thus tracking the mood changing is an effect way to automatically analyze the music mood for better music understanding. HCS (Hierarchical Classification Scheme), which automatically generates tree structure hierarchical classifier driven by training data to adaptive different recognition applications, is proposed in our paper to build recognition model of music mood. The duration of training music segments is set as 8 seconds, which is assumed to contain stable mood state within a single segment to ensure the accuracy of the HCS model. The mood in an entire piece of music is then divided into 50% overlapping 8 seconds frames, which are the same duration with the training segments in HCS model, to accomplish the mood tracking. The automatic tracking result show good accordance with general music reviews and manually labeled ground truth, and proves the effectiveness of the HCS scheme and the rational selection of 8 seconds segment duration.

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“…By classifying the emotion of Chinese and Western classical music, Wu and Xie found that feature sets of rhythm, pitch, and timbre had cross-cultural attributes [14]. By selecting a set of features related to pitch, rhythm, and timbre, Zhao et al compared MER models of Western and Chinese classical music based on a Bayesian network classifier [15]. Their research indicated that Chinese traditional music's detection rate was lower than that of Western classical music.…”

Section: Introductionmentioning

confidence: 99%

Cross‐cultural analysis of the correlation between musical elements and emotion

Wang

Wei

Da-sheng

2021

Cognitive Comp and Systems

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In a cross-cultural context, exploring musical elements' cultural specificity and universality that affect various types of music is conducive to personalised emotion recognition. In this study, high-level musical elements are introduced to explore their influence on emotional perception. By comparing music emotion recognition (MER) models of varied cultural music, musical elements with cultural universality and cultural specificity are further determined. Participants rated 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. The relationship between music emotion and musical elements was analysed through partial least squares regression. Results showed that tempo, rhythm complexity, and articulation are culturally universal; musical elements related to timbre, register, and dynamics features are culturally specific. By increasing tempo, rhythm complexity, staccato, perception of valence, tension arousal, and energy arousal can be effectively improved. Based on the Partial least squares regression (PLSR) model's results for the datasets, the combination of manual and automatic annotation for musical elements can improve the MER system's performance.

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The analysis of mood taxonomy comparision between chinese and western music

Cited by 6 publications

References 9 publications

Music Emotion Recognition: From Content- to Context-Based Models

Music Emotion Recognition: From Content- to Context-Based Models

Music mood tracking based on HCS

Cross‐cultural analysis of the correlation between musical elements and emotion

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