A Novel Method Based on OMPGW Method for Feature Extraction in Automatic Music Mood Classification

Mo, Shasha; Niu, Jianwei

doi:10.1109/taffc.2017.2724515

Cited by 30 publications

(24 citation statements)

References 36 publications

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“…The situation is less extreme in the case of valence, with 81 sounds located in quadrants 1 and 4. Nevertheless, at 167 samples, the IADS is small compared with MER, where data sets range from around 30 to over 100,000 songs [43,54]. Recognition of these limitations of the IADS might be dealt with by creation of a larger set of validated samples with a more uniform distribution.…”

Section: Discussionmentioning

confidence: 99%

“…Affective computing is an interdisciplinary research field concerned with the emotional interaction between technology and humans [48]. The field of Music Emotion Recognition (MER) is one such subset of this broad field and has received considerable attention from the research community in recent years [16,32,43,52,52,54,66]. In this article, however, we turn our focus to the area of AER, which deals with affect in non-musical sound.…”

Section: Affective Computing and Audiomentioning

confidence: 99%

“…Such an approach is typical in the field of emotion recognition. Although our work focuses upon the affective analysis of audio, it is worth making the observation that, in the field of Music Emotion Recognition (MER), it is typically reported that models for the prediction of the arousal dimension tend to outperform those of valence [32,43,52,54,54].…”

Section: Affective Computing and Audiomentioning

confidence: 99%

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Supervised machine learning for audio emotion recognition

Cunningham

Ridley

Weinel

et al. 2020

Pers Ubiquit Comput

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The field of Music Emotion Recognition has become and established research sub-domain of Music Information Retrieval. Less attention has been directed towards the counterpart domain of Audio Emotion Recognition, which focuses upon detection of emotional stimuli resulting from non-musical sound. By better understanding how sounds provoke emotional responses in an audience, it may be possible to enhance the work of sound designers. The work in this paper uses the International Affective Digital Sounds set. A total of 76 features are extracted from the sounds, spanning the time and frequency domains. The features are then subjected to an initial analysis to determine what level of similarity exists between pairs of features measured using Pearson's r correlation coefficient before being used as inputs to a multiple regression model to determine their weighting and relative importance. The features are then used as the input to two machine learning approaches: regression modelling and artificial neural networks in order to determine their ability to predict the emotional dimensions of arousal and valence. It was found that a small number of strong correlations exist between the features and that a greater number of features contribute significantly to the predictive power of emotional valence, rather than arousal. Shallow neural networks perform significantly better than a range of regression models and the best performing networks were able to account for 64.4% of the variance in prediction of arousal and 65.4% in the case of valence. These findings are a major improvement over those encountered in the literature. Several extensions of this research are discussed, including work related to improving data sets as well as the modelling processes.

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Section: Discussionmentioning

confidence: 99%

Section: Affective Computing and Audiomentioning

confidence: 99%

Section: Affective Computing and Audiomentioning

confidence: 99%

See 1 more Smart Citation

Supervised machine learning for audio emotion recognition

Cunningham

Ridley

Weinel

et al. 2020

Pers Ubiquit Comput

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show abstract

“…Self collected [17], [18], [19], [20], [21], [22] RECOLA [23] CAL500 [24], [25], [26] Magna Tag A Tune [27], [28] MSD [27], [29] AMG 1608 [30], [31], [32] MER60 [30], [33] DEAP [34], [35], [36] Mediaeval [37], [38], [39] GTZAN [16] Marsyas [40]…”

Section: Dataset Related Workmentioning

confidence: 99%

“…Authors make use of hamming [7], [42]- [44] and hanning windows [45], [46] for preprocessing the signals. Gabor function can also be used for preprocessing the signals [47]. An automated tool named Cool Edit Pro is also used to preprocess the music signals [48], [49].…”

Section: Preprocessingmentioning

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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Exploration on Automatic Recommendation System for Video Background Music Based on Machine Learning

Liu

2024

Lecture Notes on Data Engineering and Communications Technologies

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A Novel Method Based on OMPGW Method for Feature Extraction in Automatic Music Mood Classification

Cited by 30 publications

References 36 publications

Supervised machine learning for audio emotion recognition

Supervised machine learning for audio emotion recognition

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

Exploration on Automatic Recommendation System for Video Background Music Based on Machine Learning

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