Bipartite network analysis of sample-based music

Park, Dongju; Park, Juyong

doi:10.1007/s40042-023-00749-2

Cited by 2 publications

(4 citation statements)

References 25 publications

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“…In a similar fashion, we experimented with transferring the knowledge from the models presented by Park et al [12] developed for artist classification. For the purposes of this work, the simpler model was adapted, consisting of a sequence of 5 one-dimensional convolutional blocks, a global average pooling layer, and a dense layer that outputs a 256-value vector, as seen in Figure 8.…”

Section: Transfer Learningmentioning

confidence: 99%

“…Typically, raw input data are represented by a spectrogram, but end-to-end architectures that do not require previous processing have also been proposed [9,10]. In addition, learning paradigms, such as transfer learning from other domains with larger available datasets [11,12], and different data representations, such as from embeddings that can be extracted from pre-trained CNNs [13] have also been proposed.…”

Section: Introductionmentioning

confidence: 99%

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A Comparison Study of Deep Learning Methodologies for Music Emotion Recognition

Louro,

Redinho,

Malheiro

et al. 2024

Sensors

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Classical machine learning techniques have dominated Music Emotion Recognition. However, improvements have slowed down due to the complex and time-consuming task of handcrafting new emotionally relevant audio features. Deep learning methods have recently gained popularity in the field because of their ability to automatically learn relevant features from spectral representations of songs, eliminating such necessity. Nonetheless, there are limitations, such as the need for large amounts of quality labeled data, a common problem in MER research. To understand the effectiveness of these techniques, a comparison study using various classical machine learning and deep learning methods was conducted. The results showed that using an ensemble of a Dense Neural Network and a Convolutional Neural Network architecture resulted in a state-of-the-art 80.20% F1 score, an improvement of around 5% considering the best baseline results, concluding that future research should take advantage of both paradigms, that is, combining handcrafted features with feature learning.

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Section: Transfer Learningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Comparison Study of Deep Learning Methodologies for Music Emotion Recognition

Louro,

Redinho,

Malheiro

et al. 2024

Sensors

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“…Willie Nelson ranks at the top in betweenness centrality. Nelson is credited with helping to create the outlaw country subgenre [9], where he played a role in bridging the gap between country and rock music. The highest betweenness centrality suggests that he has been significant in potentially connecting the traditional country artists with the newer houtlaw generation and, hence, playing an essential role in the transition of country music.…”

Section: Betweennessmentioning

confidence: 99%

“…The widespread adoption of network science, machine learning, and deep learning models has become commonplace in this rapidly advancing technological era. As a result, researchers are leveraging these models to explore and analyze the impact of music on various aspects [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Understanding the Influence of Genre-Specific Music Using Network Analysis and Machine Learning Algorithms

Lamichhane,

Singh,

Devkota

et al. 2023

BDCC

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This study analyzes a network of musical influence using machine learning and network analysis techniques. A directed network model is used to represent the influence relations between artists as nodes and edges. Network properties and centrality measures are analyzed to identify influential patterns. In addition, influence within and outside the genre is quantified using in-genre and out-genre weights. Regression analysis is performed to determine the impact of musical attributes on influence. We find that speechiness, acousticness, and valence are the top features of the most influential artists. We also introduce the IRDI, an algorithm that provides an innovative approach to quantify an artist’s influence by capturing the degree of dominance among their followers. This approach underscores influential artists who drive the evolution of music, setting trends and significantly inspiring a new generation of artists. The independent cascade model is further employed to open up the temporal dynamics of influence propagation across the entire musical network, highlighting how initial seeds of influence can contagiously spread through the network. This multidisciplinary approach provides a nuanced understanding of musical influence that refines existing methods and sheds light on influential trends and dynamics.

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Bipartite network analysis of sample-based music

Cited by 2 publications

References 25 publications

A Comparison Study of Deep Learning Methodologies for Music Emotion Recognition

A Comparison Study of Deep Learning Methodologies for Music Emotion Recognition

Understanding the Influence of Genre-Specific Music Using Network Analysis and Machine Learning Algorithms

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