Multi-label annotation of music

Ahsan, Hiba; Kumar, Vijay; Jawahar, C. V.

doi:10.1109/icapr.2015.7050685

Cited by 6 publications

(3 citation statements)

References 19 publications

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“…As mentioned earlier, the growth of the rating system through reviews in recent years has been market-driven: when thinking about streaming services, it is evident how the need to provide suggestions that are increasingly in line with user needs has required a refinement of multi-label rating systems to identify the different genres in which to frame the products offered, such as in music [38], [39] or movie [40], [41] streaming services.…”

Section: Multiple Labels: Problem Transformation Methodsmentioning

confidence: 99%

A new Italian Cultural Heritage data set: detecting fake reviews with BERT and ELECTRA leveraging the sentiment

et al. 2023

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The growth of the online review phenomenon, which has expanded from specialised trade magazines to end users via online platforms, has also increasingly involved the cultural heritage of countries, a source of tourism and growth driver of local economies. Unfortunately, this has been paralleled by the emergence and spread of the phenomenon of fake reviews, against which the scientific world has developed language models capable of distinguishing them from the truthful. The application of such models, often based on deep neural networks with transformer-type architectures, is however limited by the availability of local language data sets for specific domains, useful for both training and verification. The purpose of this article is twofold. Firstly, a new data set was created in the Italian language, generally considered low-resource, relating to the domain of cultural heritage in Italy, by collecting reviews available online, reorganising them in the form of a data set usable by the language models. Secondly, a baseline of results for the detection of misleading reviews was constructed by exploiting two widely used language models, namely BERT and ELECTRA. The performance achieved is interesting, around 95% accuracy and F1 score, using data set splits between training and testing of 80/20 and 90/10. In addition, SHAP was used as a tool to support the explicability of AI models: in this way, it was possible to show the usefulness of sentiment analysis as a support for the recognition of deceptiveness.INDEX TERMS Italian cultural heritage, data set, fake reviews, sentiment analysis, deceptive.

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Section: Multiple Labels: Problem Transformation Methodsmentioning

confidence: 99%

A new Italian Cultural Heritage data set: detecting fake reviews with BERT and ELECTRA leveraging the sentiment

et al. 2023

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“…Users are given the choice to participate and are openly informed about the webcam access. User data is rigorously protected by privacy safeguards [15]. A safe and healing musical experience is another goal of the system, and any potential emotional triggers are carefully avoided .…”

Section: F User Interaction and Feedback Loopmentioning

confidence: 99%

Emotion-Based Music Generation Based on Nava Rasas in Indian Classical Music

Churi

2023

IJRASET

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A crucial component of human expression, music has the astonishing power to provoke a wide range of emotions. In this study, we describe a revolutionary method for creating music that integrates Indian classical music, computer vision, and emotion analysis. In our project, named "Emotion-Based Music Generation," we use Media Pipe for facial expression recognition, Keras for music creation, OpenCV for real-time webcam access, and stream lit-WebRTC for web application development. Based on the Nava Rasas in Indian classical music, the technique isolates nine fundamental emotions and develops musical compositions in accordance. These technologies are used to create an enjoyable and interactive system that allows users to explore the emotional spectrum of music.

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“…KNN classifiers are used to store the data for all the categories and new classes are classified on the basis of distance functions. The test data is classified based on the majority vote of its neighbors [87], [88]. iii) SVM classifiers are based on supervised learning techniques and algorithms.…”

Section: Feature Selection Algorithm Related Workmentioning

confidence: 99%

A Survey on Autonomous Techniques for Music Classification based on Human Emotions Recognition

Chaudhary¹,

Singh²,

Singh³

2020

IJCDS

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Music is one of the finest element to trigger emotions in human beings. Each and every human being feels the music and emotions are automatically provoked by listening music. Music is considered as strong stress reliever. With the increase in size of music dataset available online and advancement of automation technologies the emotions from the music are to be recognized automatically so that the online database of music can be organized and browsed in an efficient manner. Automation of music emotion classification (MEC) helps the people to listen the music of their interest without wasting time on surfing the internet. It helps the psychologists in treatment process of patients. It also helps the musicians and artists to work on specific type of music and to classify them. This paper aims to provide the overview and survey related to autonomous technique for music classification (ATMC). In this article, the basic steps such as database collection, preprocessing, database analysis, feature extraction, classification and evaluation parameters involved in ATMC are explained and comprehensive review related to the basic steps is summarized. Research issues and solutions related to ATMC along with future scope are also discussed in this article.

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Multi-label annotation of music

Cited by 6 publications

References 19 publications

A new Italian Cultural Heritage data set: detecting fake reviews with BERT and ELECTRA leveraging the sentiment

A new Italian Cultural Heritage data set: detecting fake reviews with BERT and ELECTRA leveraging the sentiment

Emotion-Based Music Generation Based on Nava Rasas in Indian Classical Music

A Survey on Autonomous Techniques for Music Classification based on Human Emotions Recognition

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