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This paper aims to discuss how collaborative classification works in online music information retrieval systems and its impacts on the construction, fixation and orientation of the social uses of popular music on the internet.

Using a comparative method, the paper examines the logic behind music classification in Recommender Systems by studying the case of Last.fm, one of the most popular web sites of this type on the web. Data collected about users' ritual classifications are compared with the classification used by the music industry, represented by the AllMusic web site.

The paper identifies the differences between the criteria used for the collaborative classification of popular music, which is defined by users, and the traditional standards of commercial classification, used by the cultural industries, and discusses why commercial and non‐commercial classification methods vary.

Collaborative ritual classification reveals a shift in the demand for cultural information that may affect the way in which this demand is organized, as well as the classification criteria for works on the digital music market.

Collective creation of a music classification in recommender systems represents a new model of cultural mediation that might change the way of building new uses, tastes and patterns of musical consumption in online environments.

The paper highlights the way in which the classification process might influence the behavior of the users of music information retrieval systems, and vice versa.

Music sentiment analysis helps to promote the diversification of music information retrieval methods. Traditional music emotion classification tasks suffer from high manual workload and low classification accuracy caused by difficulty in feature extraction and inaccurate manual determination of hyperparameter. In this paper, the authors propose an optimized convolution neural network-random forest (CNN-RF) model for music sentiment classification which is capable of optimizing the manually selected hyperparameters to improve the accuracy of music sentiment classification and reduce labor costs and human classification errors.

A CNN-RF music sentiment classification model is designed based on quantum particle swarm optimization (QPSO). First, the audio data are transformed into a Mel spectrogram, and feature extraction is conducted by a CNN. Second, the music features extracted are processed by RF algorithm to complete a preliminary emotion classification. Finally, to select the suitable hyperparameters for a CNN, the QPSO algorithm is adopted to extract the best hyperparameters and obtain the final classification results.

The model has gone through experimental validations and achieved a classification accuracy of 97 per cent for different sentiment categories with shortened training time. The proposed method with QPSO achieved 1.2 and 1.6 per cent higher accuracy than that with particle swarm optimization and genetic algorithm, respectively. The proposed model had great potential for music sentiment classification.

The dual contribution of this work comprises the proposed model which integrated two deep learning models and the introduction of a QPSO into model optimization. With these two innovations, the efficiency and accuracy of music emotion recognition and classification have been significantly improved.

The purpose of this paper is to understand the classification of musical medium, which is a critical part of music classification. It considers how musical medium is currently classified, provides a theoretical understanding of what is currently problematic, and proposes a model which rethinks the classification of medium and resolves these issues.

The analysis is drawn from existing classification schemes, additionally using musicological and knowledge organization literature where relevant. The paper culminates in the design of a model of musical medium.

The analysis elicits sub-facets, orders and categorizations of medium: there is a strict categorization between vocal and instrumental music, a categorization based on broad size, and important sub-facets for multiples, accompaniment and arrangement. Problematically, there is a mismatch between the definitiveness of library and information science vocal/instrumental categorization and the blurred nature of real musical works; arrangements and accompaniments are limited by other categorizations; multiple voices and groups are not accommodated. So, a model with a radical new structure is proposed which resolves these classification issues.

The results could be used to further understanding of music classification generally, for Western art music and other types of music.

The resulting model could be used to improve and design new classification schemes and to improve understanding of music retrieval.

Deep theoretical analysis of music classification is rare, so this paper’s approach is original. Furthermore, the paper’s value lies in studying a vital area of music classification which is not currently understood, and providing explanations and solutions. The proposed model is novel in structure and concept, and its original structure could be adapted for other knotty subjects.

The purpose of this paper is to present a new framework for representing music for information retrieval that emphasizes socio-cultural aspects of music.

Philosophical and theoretical concepts related to the nature of music, aboutness, musical works are explored as they inform how music is represented. Multidisciplinary perspectives on music information representation, classification, and retrieval provide insight into how information science can better accommodate music information within its disciplinary boundaries.

A new term, music information object (MIO), is presented and defined. Downie’s (2003) theoretical statements are reconceptualized into a theory of representational incompleteness and three meta-classes for music information object representation.

This new framework incorporates more dimensions of music representation than existing frameworks allow and can facilitate comparisons between classifications of MIO representations by music practitioners, scholars, and system developers.

The meta-classes form a much-needed theoretical framework for classifying and defining MIOs from any musical tradition for retrieval. This fills a gap in music information retrieval research, which lacks a theoretical framework that can accommodate musics from all traditions without attempting to organize them according to a western-centered understanding.

Producers and consumers – who represent opposing sides of the market – have different frames of reference, which may result in differences in classification of the same products. The authors aim to demonstrate that “classification gaps” have a negative effect on the performance of products and that these effects play a role in different stages of consumers’ decision process.

The data collection consisted of three comprehensive parts covering production and consumption in the music festival market in The Netherlands. The first part focused on festival organizers who were asked to classify their own music festival in terms of musical genres. In total, 70 festival organizers agreed to participate. The second part measured the genre classification of 540 consumers. In the third part, the authors interviewed 1,554 potential visitors of music festivals in The Netherlands about their awareness of the festival and if they considered visiting or actually visited the festival.

This paper provides empirical evidence that a classification gap between the production side and the consumption side of the market has negative effects on music festival performance. In addition, the authors found that this is in part because of lower activation of potential consumers in the marketplace.

An important practical implication of this study is that – in general – producers should be aware that classification gaps can occur – even if they are sure about the classification of their products – and that this can have serious consequences. The category membership of products is often seen as a given, whereas it cannot be assumed that the classification perceived by different economic groups is the same – as demonstrated in this paper.

This paper demonstrates that a fundamental – but understudied – disconnect between the two opposing sides of the market (i.e. producers and consumers) regarding the classification of the same products can have negative effects on performance of these products.

Since the arrival of mass production, commodification has been plaguing markets – none more so than that for music. By separating production and consumption in space and time, commodification challenges the very conditions underlying economic exchange. This chapter explores authenticity as the institutional response to the commodification of music, rekindling the relationship between isolated market participants in the increasingly digitized world of music. Building upon the “Production of Culture” perspective, we unpack the commodification of music across five different institutional realms – (1) production, (2) consumption, (3) selection, (4) appropriation, and (5) classification – and provide a thoroughly relational account of authenticity as an institutional practice.

In general, Indian Classical Music (ICM) is classified into two: Carnatic and Hindustani. Even though, both the music formats have a similar foundation, the way of presentation is varied in many manners. The fundamental components of ICM are raga and taala. Taala basically represents the rhythmic patterns or beats (Dandawate et al., 2015; Kirthika and Chattamvelli, 2012). Raga is determined from the flow of swaras (notes), which is denoted as the wider terminology. The raga is defined based on some vital factors such as swaras, aarohana-avarohna and typical phrases. Technically, the fundamental frequency is swara, which is definite through duration. Moreover, there are many other problems for automatic raga recognition model. Thus, in this work, raga is recognized without utilizing explicit note series information and necessary to adopt an efficient classification model.

This paper proposes an efficient raga identification system through which music of Carnatic genre can be effectively recognized. This paper also proposes an adaptive classifier based on NN in which the feature set is used for learning. The adaptive classifier exploits advanced metaheuristic-based learning algorithm to get the knowledge of the extracted feature set. Since the learning algorithm plays a crucial role in defining the precision of the raga recognition, this model prefers to use the GWO.

Through the performance analysis, it is witnessed that the accuracy of proposed model is 16.6% better than NN with LM, NN with GD and NN with FF respectively, 14.7% better than NN with PSO. Specificity measure of the proposed model is 19.6, 24.0, 13.5 and 17.5% superior to NN with LM, NN with GD, NN with FF and NN with PSO, respectively. NPV of the proposed model is 19.6, 24, 13.5 and 17.5% better than NN with LM, NN with GD, NN with FF and NN with PSO, respectively. Thus it has proven that the proposed model has provided the best result than other conventional classification methods.

This paper intends to propose an efficient raga identification system through which music of Carnatic genre can be effectively recognized. This paper also proposes an adaptive classifier based on NN.

Genre classification of songs based on lyrics is a challenging task even for humans, however, state-of-the-art natural language processing has recently offered advanced solutions to this task. The purpose of this study is to advance the understanding and application of natural language processing and deep learning in the domain of music genre classification, while also contributing to the broader themes of global knowledge and communication, and sustainable preservation of cultural heritage.

The main contribution of this study is the development and evaluation of various machine and deep learning models for song genre classification. Additionally, we investigated the effect of different word embeddings, including Global Vectors for Word Representation (GloVe) and Word2Vec, on the classification performance. The tested models range from benchmarks such as logistic regression, support vector machine and random forest, to more complex neural network architectures and transformer-based models, such as recurrent neural network, long short-term memory, bidirectional long short-term memory and bidirectional encoder representations from transformers (BERT).

The authors conducted experiments on both English and multilingual data sets for genre classification. The results show that the BERT model achieved the best accuracy on the English data set, whereas cross-lingual language model pretraining based on RoBERTa (XLM-RoBERTa) performed the best on the multilingual data set. This study found that songs in the metal genre were the most accurately labeled, as their text style and topics were the most distinct from other genres. On the contrary, songs from the pop and rock genres were more challenging to differentiate. This study also compared the impact of different word embeddings on the classification task and found that models with GloVe word embeddings outperformed Word2Vec and the learning embedding layer.

This study presents the implementation, testing and comparison of various machine and deep learning models for genre classification. The results demonstrate that transformer models, including BERT, robustly optimized BERT pretraining approach, distilled bidirectional encoder representations from transformers, bidirectional and auto-regressive transformers and XLM-RoBERTa, outperformed other models.

Using a novel measure incorporating stylistic and acoustic data on recorded music from 1967 to 2017, we search for trends in the evolution of musical diversity in 125,340 albums. We find that temporal patterns of diversity differ for stylistic and acoustic data. We also find that the patterns differ dramatically by genre. Some genres, such as blues, jazz, and pop-rock, decrease in diversity over time; most other genres increase in diversity. The causes of these different trends present a puzzle for future research. We also find different patterns for recordings that made the Billboard 200 charts compared to all recordings, suggesting an association between selection processes driven by consumer popularity and diversity. Moreover, associations of diversity and industry structure found in prior research do not hold when we analyze data beyond the smaller sample of the more popular recordings found in Billboard. These findings have implications for many prior studies based exclusively on best-selling recordings