Music genre classification based on fusing audio and lyric information

Li, You; Zhang, Zhihai; Ding, Han; Chang, Liang

doi:10.1007/s11042-022-14252-6

Cited by 8 publications

(5 citation statements)

References 44 publications

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“…The proposed model achieved a high classification accuracy of 96.50% on the GTZAN dataset. A res-gated convolutional structure with an attention mechanism was also applied to the MGC task, and the model achieved a classification accuracy of 96.8% on the GTZAN dataset [87]. Parallel attention was applied to the CNN model to extract multiple features from the MLS features [89].…”

Section: Related Workmentioning

confidence: 99%

“…Because of the remarkable success of deep learning in various fields, various deep learning models such as a graph neural network [86], a convolutional neural network [60], and an attention model [87] can be applied to the MIR domain. Recently, convolutional neural networks (CNNs) have attracted huge research interests in the MIR domain due to their inherent ability to handle complex spectral features [38], [39], [41].…”

Section: Introductionmentioning

confidence: 99%

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A Short Survey and Comparison of CNN-Based Music Genre Classification Using Multiple Spectral Features

Seo,

Cho,

Teisseyre

et al. 2024

IEEE Access

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The goal of music genre classification is to identify the genre of given feature vectors representing certain characteristics of music clips. In addition, to improve the accuracy of music genre classification, considerable research has been conducted on extracting spectral features, which contain critical information for genre classification, from music clips and feeding these features into training models. In particular, recent studies argue that classification accuracy can be enhanced by employing multiple spectral features simultaneously. Consequently, fusing information from multiple spectral features is a critical consideration in designing music genre classification models. Hence, this paper provides a short survey of recent studies on music genre classification and compares the performance of the most recent CNN-based models with a newly devised model that employs a late fusion strategy for the multiple spectral features. Our empirical study of 12 public datasets, including Ballroom, ISMIR04, and GTZAN, showed that the late fusion CNN model outperforms other compared methods. Additionally, we performed an indepth analysis to validate the effectiveness of the late fusion strategy in music genre classification.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Short Survey and Comparison of CNN-Based Music Genre Classification Using Multiple Spectral Features

Seo,

Cho,

Teisseyre

et al. 2024

IEEE Access

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“…The achieved an overall accuracy of 90.2% on a dataset of 10 genres, demonstrating the proposed method's effectiveness. Li et al (2022) proposed a method for music classification using a transformer-based neural network architecture. The achieved an overall accuracy of 92.6% on a dataset of 10 genres, showing the effectiveness of transformer-based models for music classification.…”

Section: Backgroundmentioning

confidence: 99%

Harmonizing minds and machines: survey on transformative power of machine learning in music

Liang

2023

Front. Neurorobot.

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This survey explores the symbiotic relationship between Machine Learning (ML) and music, focusing on the transformative role of Artificial Intelligence (AI) in the musical sphere. Beginning with a historical contextualization of the intertwined trajectories of music and technology, the paper discusses the progressive use of ML in music analysis and creation. Emphasis is placed on present applications and future potential. A detailed examination of music information retrieval, automatic music transcription, music recommendation, and algorithmic composition presents state-of-the-art algorithms and their respective functionalities. The paper underscores recent advancements, including ML-assisted music production and emotion-driven music generation. The survey concludes with a prospective contemplation of future directions of ML within music, highlighting the ongoing growth, novel applications, and anticipation of deeper integration of ML across musical domains. This comprehensive study asserts the profound potential of ML to revolutionize the musical landscape and encourages further exploration and advancement in this emerging interdisciplinary field.

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“…While audio components, which comprise several abstraction levels, can imply music-evoked emotions [1]; predict viral songs [14]; and measure music similarity [15]. Frequently, textual and audio features have been used together for classifying moods [16,17] and musical genres [18,19]. However, there is limited March 23, 2023 2/38 knowledge about how the combination of audio and lyrics may influence the association between psychological traits and music choices.…”

Section: Introductionmentioning

confidence: 99%

Soundscapes of morality: Linking music preferences and moral values through lyrics and audio

Preniqi¹,

Kalimeri²,

Saitis³

2023

Preprint

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Music is a fundamental element in every culture, serving as a universal means of expressing our emotions, feelings, and beliefs. This work investigates the link between our moral values and musical choices through lyrics and audio analyses. We align the psychometric scores of 1,480 participants to acoustics and lyrics features obtained from the top 5 songs of their preferred music artists from Facebook Page Likes. We employ a variety of lyric text processing techniques, including lexicon-based approaches and BERT-based embeddings, to identify each song's narrative, moral valence, attitude, and emotions. In addition, we extract both low- and high-level audio features to comprehend the encoded information in participants' musical choices and improve the moral inferences. We propose a Machine Learning approach and assess the predictive power of lyrical and acoustic features separately and in a multimodal framework for predicting moral values. Results indicate that lyrics and audio features from the artists people like inform us about their morality. Though the most predictive features vary per moral value, the models that utilised a combination of lyrics and audio characteristics were the most successful in predicting moral values, outperforming the models that only used basic features such as user demographics, the popularity of the artists, and the number of likes per user.Audio features boosted the accuracy in the prediction of empathy and equality compared to textual features, while the opposite happened for hierarchy and tradition, where higher prediction scores were driven by lyrical features. This demonstrates the importance of both lyrics and audio features in capturing moral values.The insights gained from our study have a broad range of potential uses, including customising the music experience to meet individual needs, music rehabilitation, or even effective communication campaign crafting.

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Music genre classification based on fusing audio and lyric information

Cited by 8 publications

References 44 publications

A Short Survey and Comparison of CNN-Based Music Genre Classification Using Multiple Spectral Features

A Short Survey and Comparison of CNN-Based Music Genre Classification Using Multiple Spectral Features

Harmonizing minds and machines: survey on transformative power of machine learning in music

Soundscapes of morality: Linking music preferences and moral values through lyrics and audio

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