Speech/music classification using visual and spectral chromagram features

Birajdar, Gajanan K.; Patil, Mukesh D.

doi:10.1007/s12652-019-01303-4

Cited by 36 publications

(21 citation statements)

References 53 publications

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“…Skewness is a measure of the asymmetry of a signal and can represent the relative tendency of tonal and nontonal components in the band. For a sequence x of length N, the mean is 􏽐 N n�1 (x n − u) 3 and the standard deviation is σ. e mathematical expression of skewness is as follows:…”

Section: Skewness and Kurtosismentioning

confidence: 99%

“…With the continuous development of Internet technology and the improvement of the technological level, different Internet-based music multimedia began to emerge. Music multimedia is one of the more popular types of digital music, so its automatic classification and recognition has become the focus of domestic scholars' research [1][2][3]. However, how to obtain the specific content source from the initial music multimedia data, lacking the definition of the music content, has become a huge challenge for the current automatic classification of music multimedia because the music multimedia signal belongs to a way of time sequence, which can be concealed according to its concealment.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Classification and Analysis of Music Multimedia Combined with Hidden Markov Model

Chen

2021

Advances in Multimedia

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Music multimedia is one of the more popular types of digital music. This article is based on the hidden Markov model (HMM) and proposed this kind of music multimedia automatic classification method. The method not only analyzes the characteristics of traditional music in detail but also fully considers the important characteristics of other music. At the same time, it uses bagging to train two groups of HMMs and automatically classifies them to achieve a better classification effect. This paper optimizes the variable parameters from different aspects such as model structure, data form, and model change to obtain the optimal HMM parameter value. This method not only considers the prior knowledge of feature words, word frequency, and number of documents but also fuses the meaning of the feature words into the hidden Markov classification model. Finally, by testing the hidden Markov model used in this paper on the music multimedia data set, the experimental results show that the method in this paper can effectively perform automatic classification according to the melody characteristics of music multimedia.

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Section: Skewness and Kurtosismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic Classification and Analysis of Music Multimedia Combined with Hidden Markov Model

Chen

2021

Advances in Multimedia

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“…Table 6 lists all the extracted features along with their statistical measures of mean and standard deviation (STD) for method I (DWT) and method II (EMD). We extracted time domain [ 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 ], spectral [ 46 , 47 ], fractal and chaos [ 48 , 49 ], chroma [ 50 , 51 ], cepstral [ 52 ], and texture features [ 53 ] and analyzed them statistically.…”

Section: Methodsmentioning

confidence: 99%

Expert Hypertension Detection System Featuring Pulse Plethysmograph Signals and Hybrid Feature Selection and Reduction Scheme

Khan

Aziz

Akram

et al. 2021

Sensors

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Hypertension is an antecedent to cardiac disorders. According to the World Health Organization (WHO), the number of people affected with hypertension will reach around 1.56 billion by 2025. Early detection of hypertension is imperative to prevent the complications caused by cardiac abnormalities. Hypertension usually possesses no apparent detectable symptoms; hence, the control rate is significantly low. Computer-aided diagnosis based on machine learning and signal analysis has recently been applied to identify biomarkers for the accurate prediction of hypertension. This research proposes a new expert hypertension detection system (EHDS) from pulse plethysmograph (PuPG) signals for the categorization of normal and hypertension. The PuPG signal data set, including rich information of cardiac activity, was acquired from healthy and hypertensive subjects. The raw PuPG signals were preprocessed through empirical mode decomposition (EMD) by decomposing a signal into its constituent components. A combination of multi-domain features was extracted from the preprocessed PuPG signal. The features exhibiting high discriminative characteristics were selected and reduced through a proposed hybrid feature selection and reduction (HFSR) scheme. Selected features were subjected to various classification methods in a comparative fashion in which the best performance of 99.4% accuracy, 99.6% sensitivity, and 99.2% specificity was achieved through weighted k-nearest neighbor (KNN-W). The performance of the proposed EHDS was thoroughly assessed by tenfold cross-validation. The proposed EHDS achieved better detection performance in comparison to other electrocardiogram (ECG) and photoplethysmograph (PPG)-based methods.

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“…At present, it is mainly used to combine the recursive K-mean algorithm and the recursive least squares method, which has achieved certain results, but more effective improvements are needed. e above issues will further promote the application of RBF networks in various fields [10].…”

Section: Introductionmentioning

confidence: 99%

Multimedia Vocal Performance Automation Evaluation Model Based on RBF Network

Wang

2022

Mathematical Problems in Engineering

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Aiming at the problems of the radial basis network model, this paper proposes a multimedia vocal singing automation evaluation network model, combined with the characteristics of multimedia modeling innovation design, and proposes a two-level comprehensive model. First of all, the theory and algorithm of analytic hierarchy process and radial basis function network are researched and analyzed, and the RBF is predicted for the mature area of multimedia development based on the three indicators of the total amount of classified vocals. The prediction scheme evaluation system is then used to fit the prediction data and influencing factors using the RBF network, and then the classified vocals are adjusted and synthesized hierarchically, and a multimedia vocal classification prediction model is established. Finally, this paper uses an example to verify the feasibility performance and prediction accuracy. Based on the above theory, the experiment uses VC++ 6.0 and Matlab 6.5 combined with database technology to initially realize the evaluation system and achieves a good evaluation effect. The simulation results show that three different algorithms are used to establish RBFO content prediction models. The correlation coefficient limit, root mean square error prediction, and relative analysis error (RED) reached 0.9937, 15.5095, and 8.216, respectively. At the same time, the evaluation results have high accuracy and credibility, which not only provide designers with ideas and improvement basis for innovative designs but also ensure design quality, improve design efficiency, and show that RBF networks have good generalization capabilities.

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Speech/music classification using visual and spectral chromagram features

Cited by 36 publications

References 53 publications

Automatic Classification and Analysis of Music Multimedia Combined with Hidden Markov Model

Automatic Classification and Analysis of Music Multimedia Combined with Hidden Markov Model

Expert Hypertension Detection System Featuring Pulse Plethysmograph Signals and Hybrid Feature Selection and Reduction Scheme

Multimedia Vocal Performance Automation Evaluation Model Based on RBF Network

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