Musical emotion perception in bimodal patients: Relationship between bimodal benefit and neural representation of temporal fine structure using Rhodes piano stimuli

D'Onofrio, Kristen L.; Smith, Spencer; Kessler, David; Williams, Grace; Gifford, René H.

doi:10.1121/1.5101793

Cited by 5 publications

(4 citation statements)

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“…Hence, the article firstly establishes an N-grams indexing database and constructs a music feature indexing based on the Learning N-grams algorithm database, which contains only soprano parts, and finally tests the algorithm constructed in this paper. In the literature [19], the combination of binaural electrical and acoustic hearing can bring significant "dual-modal hearing" advantages for speech recognition, sound quality and music perception. In this study, we examined the relationship between bimodal benefits of music emotion perception and neural representations of F0 using Rhodes piano stimuli at the following fundamental frequencies: 98 Hz (G2), 262 Hz (C4), and 440 Hz (A4).…”

Section: Relevant Studiesmentioning

confidence: 99%

Piano Playing Gesture Recognition Based on Multiple Intelligences Theory

Zhang

2023

Applied Mathematics and Nonlinear Sciences

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In this paper, firstly, in order to solve the piano playing recognition problem in the field of artificial intelligence, based on the theory of multiple intelligences, the VGG-16 deep network migration learning algorithm is applied to estimate and acquire the piano playing gesture posture. Secondly, combined with the Iterative Update Extended Kalman Filter (IUEKF) algorithm, the micro-inertial sensor fixation of the piano-playing gesture is realized, which in turn is conducive to improving the piano-playing gesture recognition accuracy. Then, we obtain real-time piano-playing gesture information through a Kinect somatosensory device, construct a piano-playing gesture recognition model based on migration learning on the basis of obtaining piano-playing gesture features, and confirm the effectiveness of the model through the experimental study of piano-playing recognition. The results show that in piano-playing gesture recognition, the recognition accuracy of this paper’s method remains above 0.9, and the application of this paper’s method can effectively improve the recognition accuracy of piano-playing gestures. On piano playing pedal action recognition, this paper’s method shows that the average F-measure scores of these two strategies are 0.924 and 0.944, respectively, which are better compared to other methods. This study provides an effective case for applying AI techniques to piano performance recognition and broadens the scope of AI applications.

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Section: Relevant Studiesmentioning

confidence: 99%

Piano Playing Gesture Recognition Based on Multiple Intelligences Theory

Zhang

2023

Applied Mathematics and Nonlinear Sciences

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“…Each EEG data consists of 16 segments of data in the same format, each segment mainly contains three parts of EEG data: the resting state of EEG signal before listening to music (ideally, the subject has no emotional activity at this time, no intense brain activity, and is in a calm state), EEG signals under longterm musical stimulation (ideally, this EEG data includes the brain's perception of music, the brain response to changes in music characteristics, the generation of emotions and so on), EEG signals that re-enter the resting state after listening to music (ideally, brain activity in this state should transition from an active state stimulated by music to a resting state) (D'Onofrio et al, 2018(D'Onofrio et al, , 2019.…”

Section: Data Compositionmentioning

confidence: 99%

The aesthetic emotional expression of piano music art in the background of Internet of things

Zhang

Kang

2022

Front. Psychol.

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Artwork, generally refers to the work of plastic art. Artwork is divided into many categories, pottery art, traditional Chinese painting, abstract painting, musical instrument, sculpture, cultural relic sculpture, sandstone, imitation sandstone, ornaments, iron art, copper art, stainless steel sculpture and so on. With the continuous influx of artistic works, there are more and more studies on their emotional expression. How to judge whether musical works can bring joy, anger, sadness and joy to people? Is it joy over anger or anger over joy? Now in the era of the Internet of Things, the Internet of Things uses various information sensors, radio frequency identification technology, GPS, infrared sensors, laser scanners and other equipment and technologies to collect any objects and processes that need to be monitored, connected, and interacted in real time. By collecting various information such as sound, light, heat, electricity, mechanics, chemistry, biology, location and so on, and using various possible networks to connect, it can achieve intelligent perception, identification and management of objects and processes. The Internet of Things is an information carrier based on the Internet, traditional telecommunication networks and so on., so that all normal physical objects that can be individually located which can be connected together. The application field of the Internet of Things involves all aspects. The application in the fields of industry, agriculture, environment, transportation, logistics, security and other infrastructure has effectively promoted the intelligent development of these aspects, which making the limited resources more rational use and distribution, thus improving the efficiency and benefit of the industry. The application in household, medical and health, education, finance and service industry, tourism and other fields closely related to life has been greatly improved in terms of service scope, service method and service quality, which has greatly improved people’s quality of life. Based on this, this paper mainly studies the aesthetic emotion expression analysis of piano music art in the context of the Internet of Things. It mainly talks about the classification of music characteristics, emotional theoretical models, and emotional induction methods. Finally, the experimental analysis of piano music and the use of brain wave technology are used to analyze the experimental data. The experimental results show that in the process of feature extraction and optimization, this paper optimizes the traditional feature extraction based on power spectral density through cognitive rules, and achieves the effect of feature dimension reduction on the basis of ensuring the recognition rate. This paper uses the topological properties of EEG to classify emotions. The results show that the emotion recognition rate under the four emotional states can reach 67.3%, which is much higher than the current highest level.

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“…39 Benefits of bimodal hearing include better speech perception in quiet and noise, 40 better localization and spatial segregation of sound, 41 and improved music appreciation. [42][43][44] Despite these benefits to the "average" bimodal listener, there is considerable variability in bimodal benefit across individual patients. Factors related to patient success with a bimodal configuration are poorly understood and are not well explained by residual hearing sensitivity in the nonimplanted ear or by other psychoacoustic abilities such as temporal or spectral processing.…”

Section: Vignette 2: Objective Verification Of Vowel Discrimination I...mentioning

confidence: 99%

Translational Applications of Machine Learning in Auditory Electrophysiology

Smith

2022

Semin Hear

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Machine learning (ML) is transforming nearly every aspect of modern life including medicine and its subfields, such as hearing science. This article presents a brief conceptual overview of selected ML approaches and describes how these techniques are being applied to outstanding problems in hearing science, with a particular focus on auditory evoked potentials (AEPs). Two vignettes are presented in which ML is used to analyze subcortical AEP data. The first vignette demonstrates how ML can be used to determine if auditory learning has influenced auditory neurophysiologic function. The second vignette demonstrates how ML analysis of AEPs may be useful in determining whether hearing devices are optimized for discriminating speech sounds.

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Musical emotion perception in bimodal patients: Relationship between bimodal benefit and neural representation of temporal fine structure using Rhodes piano stimuli

Cited by 5 publications

References 0 publications

Piano Playing Gesture Recognition Based on Multiple Intelligences Theory

Piano Playing Gesture Recognition Based on Multiple Intelligences Theory

The aesthetic emotional expression of piano music art in the background of Internet of things

Translational Applications of Machine Learning in Auditory Electrophysiology

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