Mining movies for song sequences with video based music genre identification system

Doudpota, Sher Muhammad; Guha, Sumanta; Baber, Junaid

doi:10.1016/j.ipm.2012.09.005

Cited by 5 publications

(4 citation statements)

References 23 publications

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“…The Discrete Fourier Transform (DFT) technique has been previously used in contexts such as patterns recognition in data mining [34,35], and to predict the popularity of videos by analyzing videos view count traces in the frequency domain [36]. In the Web search engine application domain, it has been used for determining (i) document relevance [37], (ii) document semantic representation [38] and (iii) document classification [39].…”

Section: Related Workmentioning

confidence: 99%

A DFT-Based Running Time Prediction Algorithm for Web Queries

Rojas

Gil-Costa

2021

Future Internet

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Web search engines are built from components capable of processing large amounts of user queries per second in a distributed way. Among them, the index service computes the top-k documents that best match each incoming query by means of a document ranking operation. To achieve high performance, dynamic pruning techniques such as the WAND and BM-WAND algorithms are used to avoid fully processing all of the documents related to a query during the ranking operation. Additionally, the index service distributes the ranking operations among clusters of processors wherein in each processor multi-threading is applied to speed up query solution. In this scenario, a query running time prediction algorithm has practical applications in the efficient assignment of processors and threads to incoming queries. We propose a prediction algorithm for the WAND and BM-WAND algorithms. We experimentally show that our proposal is able to achieve accurate prediction results while significantly reducing execution time and memory consumption as compared against an alternative prediction algorithm. Our proposal applies the discrete Fourier transform (DFT) to represent key features affecting query running time whereas the resulting vectors are used to train a feed-forward neural network with back-propagation.

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Section: Related Workmentioning

confidence: 99%

A DFT-Based Running Time Prediction Algorithm for Web Queries

Rojas

Gil-Costa

2021

Future Internet

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“…In 2013, Doudpota et al (2013) have proposed a method for extracting the musical sequences from movies. In this particular work, Support vector machine (SVM) classifier along with the probabilistic timed automaton was used to categorize the musical sequences from the nonmusical frames.…”

Section: Literature Review 21 Related Workmentioning

confidence: 99%

“…Finally, the proposed system was tested with different classification algorithms and showed promising results. (Costa et al, 2012;Doudpota et al, 2013;Fu et al, 2011;Herremans et al, 2015;Liu et al, 2014;Ren et al, 2015), logistic regression (Herremans et al, 2015), if-then rule set (Herremans et al, 2015), decision tree (Herremans et al, 2015), threshold-based classifier (Khonglah and Mahadeva Prasanna, 2016) and k-NN classifier (Chen and Wang, 2013) (Costa et al, 2012;Doudpota et al, 2013;Fu et al, 2011;Herremans et al, 2015;Liu et al, 2014;Ren et al, 2015), need to find the best threshold values (Khonglah and Mahadeva Prasanna, 2016), complex calculation (Chen and Wang, 2013;Herremans et al, 2015), computationally expensive (Chen and Wang, 2013). Moreover, the reported papers varied with feature extraction.…”

Section: Literature Review 21 Related Workmentioning

confidence: 99%

“…Moreover, the reported papers varied with feature extraction. In Doudpota et al (2013), to recognize a movie from the digital archive type, the author has to extract the musical sequence. In Khonglah and Mahadeva Prasanna (2016), the author has extracted features such as vocal tract type, source of excitation and syllabic rate to recognize music and speech.…”

Section: Literature Review 21 Related Workmentioning

confidence: 99%

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Recognizing ragas of Carnatic genre using advanced intelligence: a classification system for Indian music

Kumaraswamy

Poonacha

2020

DTA

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PurposeIn 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.Design/methodology/approachThis 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.FindingsThrough 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.Originality/valueThis 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.

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A Survey of Evaluation in Music Genre Recognition

Sturm

2014

Adaptive Multimedia Retrieval: Semantics, Context, and Adaptation

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Abstract. Much work is focused upon music genre recognition (MGR) from audio recordings, symbolic data, and other modalities. While reviews have been written of some of this work before, no survey has been made of the approaches to evaluating approaches to MGR. This paper compiles a bibliography of work in MGR, and analyzes three aspects of evaluation: experimental designs, datasets, and figures of merit.

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Mining movies for song sequences with video based music genre identification system

Cited by 5 publications

References 23 publications

A DFT-Based Running Time Prediction Algorithm for Web Queries

A DFT-Based Running Time Prediction Algorithm for Web Queries

Recognizing ragas of Carnatic genre using advanced intelligence: a classification system for Indian music

A Survey of Evaluation in Music Genre Recognition

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