Classification of Music Mood Using MPEG-7 Audio Features and SVM with Confidence Interval

Sarno, Riyanarto; Ridoean, Johanes Andre; Sunaryono, Dwi; Wijaya, Dedy Rahman

doi:10.1142/s0218213018500161

Cited by 20 publications

(8 citation statements)

References 28 publications

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“…MPEG-7 audio features are derived from an international multimedia content description standard based on ISO/IEC 15938, which is composed of video and audio parts [25]. Because of the high differentiation of the MPEG-7 features in acoustic research, they have been applied in various acoustic research, such as music mood classification [40], behavioral recognition of animals [41] and voice pathology detection [42]. Therefore, the MPEG-7 LLDs extractor is used to obtain the audio features both from the raw speech signals and the corresponding glottal source waveforms.…”

Section: Extraction Of Audio Features Using Mpeg-7 Standardmentioning

confidence: 99%

Investigation and Evaluation of Glottal Flow Waveform for Voice Pathology Detection

Zhou

Fan

et al. 2021

IEEE Access

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Section: Extraction Of Audio Features Using Mpeg-7 Standardmentioning

confidence: 99%

Investigation and Evaluation of Glottal Flow Waveform for Voice Pathology Detection

Zhou

Fan

et al. 2021

IEEE Access

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“…Some scholars also have a great influence on music signal segmentation algorithms based on active boundaries. Later, many scholars extended their work with geometric partial differential equations [15][16][17]. Scholars used stochastic differential equations to study the noise spectral density of switched capacitor circuits, linear and nonlinear time-varying circuits, and stochastic differential equations to study the phase noise model of oscillators [18][19][20].…”

Section: Related Workmentioning

confidence: 99%

Using Nonlinear Diffusion Model to Identify Music Signals

2021

Advances in Mathematical Physics

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In this paper, combined with the partial differential equation music signal smoothing model, a new music signal recognition model is proposed. Experimental results show that this model has the advantages of the above two models at the same time, which can remove noise and enhance music signals. This paper also studies the music signal recognition method based on the nonlinear diffusion model. By distinguishing the flat area and the boundary area of the music signal, a new diffusion coefficient equation is obtained by combining these two methods, and the corresponding partial differential equation is discretized by the finite difference method with numerical solution. The application of partial differential equations in music signal processing is a relatively new topic. Because it can accurately model the music signal, it solves many complicated problems in music signal processing. Then, we use the group shift Fourier transform (GSFT) to transform this partial differential equation into a linear homogeneous differential equation system, and then use the series to obtain the solution of the linear homogeneous differential equation system, and finally use the group shift inverse Fourier transform to obtain the noise frequency modulation time-dependent solution of the probability density function of the interference signal. This paper attempts to use the mathematical method of stochastic differentiation to solve the key problem of the time-dependent solution of the probability density function of noise interference signals and to study the application of random differentiation theory in radar interference signal processing and music signal processing. At the end of the thesis, the application of stochastic differentiation in the filtering processing of music signals is tried. According to the inherent self-similarity of the music signal system and the completeness and stability of the empirical mode decomposition (EMD) algorithm, a new kind of EMD music using stochastic differentiation is proposed for signal filtering algorithm. This improved anisotropic diffusion method can maintain and enhance the boundary while smoothing the music signal. The filtering results of the actual music signal show that the algorithm is effective.

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“…By knowing these negative moods, the present research helps to determine in which mood the person is in thereby enabling an individual to come back to the normal mental state. Music has a unique strength to establish one's attitude and also improves the concentration of individuals' which impacts positivity in everyone's life [9]. This research is organized as follows: Section 2 presents a literature survey of the existing methods involved for mood identification in ragas.…”

Section: Introductionmentioning

confidence: 99%

Automatic Music Mood Classification using Multi-class Support Vector Machine based on Hybrid Spectral Features

Krishnaiah¹,

Parameshachari²

2021

IJIES

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Automatic Raga identification is an important step in computational musicology as it is helpful for indexing Indian music, classifying, recommending tunes, etc. However, automatic raga recognition is a difficult task as it requires guidance from the expertise training and each of the ragas is identified based on the characteristic phrase and pitch collection. However, the identification or extraction of appropriate musical sample features was difficult to enhance the success rate of the classifier. The feature identification and extraction were considered as the major issue in the existing models. To overcome such an issue, the proposed hybrid spectral feature extraction technique extracted and combined the spectral features of audios such as Spread, centroid, skewness, Mel Frequency Cepstrum Coefficients (MFCCs) and Linear Predictor Coefficients (LPCs) reduced the dimensionality complexity enhanced the success rate of the Multi-Support Vector Machine (MSVM) classifier for mood classification. The CompMusic dataset is utilized for this research work, where 4 types of ragas namely Sindhu Bhairavi, Darbari, Saveri, and Sri Raga, are considered for the mood classification of ragas. From these ragas, tone based features are extracted based on the amplitude of each raga samples and are fed to the MSVM classifier for mood classification of 4 different types are Sympathy, Serious, Peace, and Sad. The classification results for the proposed hybrid spectral feature extraction obtained better accuracy of 97.53% when compared to the existing Hidden Markov Model (HMM) obtained accuracy as 95.3%, Convolution Neural Network (CNN) model as 94%, and Gaussian Mixture Model (GMM) as 95 %.

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Classification of Music Mood Using MPEG-7 Audio Features and SVM with Confidence Interval

Cited by 20 publications

References 28 publications

Investigation and Evaluation of Glottal Flow Waveform for Voice Pathology Detection

Investigation and Evaluation of Glottal Flow Waveform for Voice Pathology Detection

Using Nonlinear Diffusion Model to Identify Music Signals

Automatic Music Mood Classification using Multi-class Support Vector Machine based on Hybrid Spectral Features

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