Computational models of music perception and cognition I: The perceptual and cognitive processing chain

Purwins, H.‐G.; Herrera, Perfecto; Grachten, Maarten; Hazan, Amaury; Marxer, Ricard; Serra, Xavier

doi:10.1016/j.plrev.2008.03.004

Cited by 25 publications

(21 citation statements)

References 91 publications

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“…With the cognitive approach, an attempt is made to model the human perceptual process by emulating the constraints of the human auditory system (e.g., auditory periphery, transformation of the signal within the cochlea, etc.) and by incorporating aspects of attention and memory processes known to be involved in the processing of music (see recent reviews in Purwins, Grachten, Herrera, Hazan, Marxer, & Serra, 2008; Purwins, Herrera, Grachten, Hazan, Marxer, & Serra, 2008). A summary of these processes and the categories involved in feature extraction is shown in Fig.…”

Section: Principles Of Model Constructionmentioning

confidence: 99%

Modeling Listeners’ Emotional Response to Music

Eerola

2012

Topics in Cognitive Science

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An overview of the computational prediction of emotional responses to music is presented. Communication of emotions by music has received a great deal of attention during the last years and a large number of empirical studies have described the role of individual features (tempo, mode, articulation, timbre) in predicting the emotions suggested or invoked by the music. However, unlike the present work, relatively few studies have attempted to model continua of expressed emotions using a variety of musical features from audio-based representations in a correlation design. The construction of the computational model is divided into four separate phases, with a different focus for evaluation. These phases include the theoretical selection of relevant features, empirical assessment of feature validity, actual feature selection, and overall evaluation of the model. Existing research on music and emotions and extraction of musical features is reviewed in terms of these criteria. Examples drawn from recent studies of emotions within the context of film soundtracks are used to demonstrate each phase in the construction of the model. These models are able to explain the dominant part of the listeners' self-reports of the emotions expressed by music and the models show potential to generalize over different genres within Western music. Possible applications of the computational models of emotions are discussed.

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Section: Principles Of Model Constructionmentioning

confidence: 99%

Modeling Listeners’ Emotional Response to Music

Eerola

2012

Topics in Cognitive Science

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“…The notion of configural perception, discussed in the sensory field [e.g. in audition (Purwins et al, 2008); vision (Noudoost et al, 2005); audition and vision (Kubovy and Van Valkenburg, 2001); gustation and olfaction (Batson et al, 2008)], according which stimulus compounds (mixtures) are represented by their components and an additional configural element that represents the conjunction of those stimuli, is consistent with blending process in odour mixture. The present study confirms for rabbit pups that the blending effect of the AB mixture is incomplete (Dreumont-Boudreau et al, 2006), and thus that the configural perception of AB is weak (Kay et al, 2005), as was previously hypothesized (Coureaud et al, 2008).…”

Section: G Coureaud and Othersmentioning

confidence: 99%

Elemental and configural processing of odour mixtures in the newborn rabbit

Coureaud

Hamdani

Schaal

et al. 2009

Journal of Experimental Biology

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SUMMARYThe processing of odour mixtures by young organisms is poorly understood. Recently, the perception of an AB mixture, known to engage configural perception in adult humans, was suggested also to be partially configural in newborn rabbits. In particular, pups did not respond to AB after they had learned A or B. However, two alternative hypotheses might be suggested to explain this result: the presence in the mixture of a novel odorant that inhibits the response to the learned stimulus, and the unevenness of the sensory and cognitive processes engaged during the conditioning and the behavioural testing. We conducted four experiments to explore these alternative hypotheses. In experiment 1, the learning of A or B ended in responses to mixtures including a novel odorant (AC or BC). Experiment 2 pointed to the absence of overshadowing. Therefore, a novelty effect cannot explain the non-response to AB after the learning of A or B. In experiment 3, pups having learned A or B in AC or BC did not respond to AB. However, they generalized odour information acquired in AB to AC or BC in experiment 4. Thus, the balancing of the perceptual tasks between the conditioning and retention test does not enhance the response to the AB mixture. To sum up, the present experiments give concrete support to the partially configural perception of specific odour mixtures by newborn rabbits.

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“…Models. There are four criteria for a valuable model [22]. They are generalization (goodness-offit [21]) and predictive power, simplicity, and its relation to existing theories.…”

Section: Criteria For Goodmentioning

confidence: 99%

“…In a valuable model, four elementary properties are goodness-of-fit [21] (generalization [22]), simplicity [21,22], predictability [21,22], and the relation to existing theories [22]. Beyond cognitive reactions, the prediction of emotional reactions is also important in music psychology [22,23]. In addition to an existing review [24], a survey of the models for emotional responses is given in this 2 Advances in Electrical Engineering paper.…”

Section: Introductionmentioning

confidence: 99%

Musical Rhythms Affect Heart Rate Variability: Algorithm and Models

Wang

Huang

2014

Advances in Electrical Engineering

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There were a lot of psychological music experiments and models but there were few psychological rhythm experiments and models. There were a lot of physiological music experiments but there were few physiological music models. There were few physiological rhythm experiments but there was no physiological rhythm model. We proposed a physiological rhythm model to fill this gap. Twenty-two participants, 4 drum loops as stimuli, and electrocardiogram (ECG) were employed in this work. We designed an algorithm to map tempo, complexity, and energy into two heart rate variability (HRV) measures, the standard deviation of normal-to-normal heartbeats (SDNN) and the ratio of low- and high-frequency powers (LF/HF); these two measures form the physiological valence/arousal plane. There were four major findings. Initially, simple and loud rhythms enhanced arousal. Secondly, the removal of fast and loud rhythms decreased arousal. Thirdly, fast rhythms increased valence. Finally, the removal of fast and quiet rhythms increased valence. Our work extended the psychological model to the physiological model and deepened the musical model into the rhythmic model. Moreover, this model could be the rules of automatic music generating systems.

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Computational models of music perception and cognition I: The perceptual and cognitive processing chain

Cited by 25 publications

References 91 publications

Modeling Listeners’ Emotional Response to Music

Modeling Listeners’ Emotional Response to Music

Elemental and configural processing of odour mixtures in the newborn rabbit

Musical Rhythms Affect Heart Rate Variability: Algorithm and Models

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