The migration of musical instruments: On the socio-technological conditions of musical evolution

Magnusson, Thor

doi:10.1080/09298215.2021.1907420

Cited by 25 publications

(16 citation statements)

References 17 publications

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“…Khulusi [12] based on the principle of Bernoulli distribution aimed to annotate images and texts. Magnusson [13] based on deep learning RBM (Restricted Boltzmann Machines) and DBN (Deep Belief Network) aimed to provide a common feature representation for multimodal data and apply it in various recognition fields. Partesotti [14] used the mutual mapping between text and image to find the mutual relationship between them, and then carried out text identification on the image.…”

Section: Related Workmentioning

confidence: 99%

Innovative Construction of Reinforcement Learning Model for Information Fusion in Music Education

Zhang

2022

Security and Communication Networks

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In order to improve the effect of modern music education, this paper applies the digital information technology of music resources to the construction of the music teaching system and derives two new types of semi-decision-making process reinforcement learning algorithms based on the Bellman optimality equation base on discrete time. Moreover, this paper uses the comparative research methods to obtain the Q-value learning curve of the incremental value iterative reinforcement learning algorithm based on the semi-Markov decision process and the incremental value iterative reinforcement learning algorithm based on the dichotomy to improve the fusion effect of music teaching resources. Finally, this paper combines the actual needs of modern music education to construct an intelligent music teaching model.

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Section: Related Workmentioning

confidence: 99%

Innovative Construction of Reinforcement Learning Model for Information Fusion in Music Education

Zhang

2022

Security and Communication Networks

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“…In practical applications, the primary task of all commercial music databases and mp3 music download sites is to collect this music into the databases of different music types. Traditional manual retrieval methods can no longer satisfy the retrieval and classification of massive information [13]. It can use the acoustic characteristics of music itself to automatically classify it, instead of manual methods.…”

Section: Related Workmentioning

confidence: 99%

Music Genre Classification Algorithm Based on Multihead Attention Mechanism

Cheng

2022

Advances in Multimedia

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Retrieving music information is indispensable and divided into multiple genres. Music genres can be attributed to set categories, which are the indispensable functions of intelligent music recommendation systems. To improve the effect of music genre classification and model construction, combined with the music genre classification algorithm, this paper combines the multihead attention mechanism to study the music genre classification algorithm model, and it analyzes the key technology of music beamforming. Moreover, this paper has made a detailed description and derivation of the array antenna model, the principle of music beamforming, and the performance evaluation criteria of music adaptive beamforming. In the second half, the nonblind classical LMS algorithm, RLS algorithm, and variable step size LMS algorithm of adaptive beamforming are studied in detail. A music genre classification algorithm model based on the multihead attention mechanism is constructed. It can be seen from the experimental research that the music genre classification algorithm based on the multihead attention mechanism proposed in this paper has obvious advantages compared with the traditional algorithm, and it has a certain role in music genre classification.

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“…Literature [16] proposed music clustering based on similarity analysis of music content and tried to apply it to the automatic generation of playlists of music playback software. Due to the importance of music similarity measurement to the above various studies, literature [17] systematically studied the problem of music similarity measurement. Literature [18] further proposed methods of real-time music understanding based on various music scene recognition research.…”

Section: Related Workmentioning

confidence: 99%

Digital Music Waveform Analysis and Retrieval Based on Feature Extraction Algorithm

Jiao

2021

Advances in Multimedia

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In order to improve the feature extraction effect of digital music and improve the efficiency of music retrieval, this paper combines digital technology to analyze music waveforms, extract music features, and realize digital processing of music features. Taking the extraction of waveform music file features as the starting point, this paper combines the digital music feature extraction algorithm to build a music feature extraction model and conducts an in-depth analysis of the digital music waveform extraction process. In addition, by setting the threshold, the linear difference between the sampling points on both sides of the threshold on the leading edge of the waveform is used to obtain the overthreshold time. From the experimental research results, it can be seen that the music feature extraction model based on digital music waveform analysis proposed in this paper has good results.

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The migration of musical instruments: On the socio-technological conditions of musical evolution

Cited by 25 publications

References 17 publications

Innovative Construction of Reinforcement Learning Model for Information Fusion in Music Education

Innovative Construction of Reinforcement Learning Model for Information Fusion in Music Education

Music Genre Classification Algorithm Based on Multihead Attention Mechanism

Digital Music Waveform Analysis and Retrieval Based on Feature Extraction Algorithm

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