MPEG-7 Features Research Articles

Music Popularity: Metrics, Characteristics, and Audio-Based Prediction

Understanding music popularity is important not only for the artists who create and perform music but also for the music-related industry. It has not been studied well how music popularity can be defined, what its characteristics are, and whether it can be predicted, which are addressed in this paper. We first define eight popularity metrics to cover multiple aspects of popularity. Then, the analysis of each popularity metric is conducted with long-term real-world chart data to deeply understand the characteristics of music popularity in the real world. We also build classification models for predicting popularity metrics using acoustic data. In particular, we focus on evaluating features describing music complexity together with other conventional acoustic features including MPEG-7 and Mel-frequency cepstral coefficient (MFCC) features. The results show that, although room still exists for improvement, it is feasible to predict the popularity metrics of a song significantly better than random chance based on its audio signal, particularly using both the complexity and MFCC features.

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

Psychologically, music can affect human mood and influence human behavior. In this paper, a novel method for music mood classification is introduced. In the experiment, music mood classification was performed using feature extraction based on MPEG-7 features from the ISO/IEC 15938 standard for describing multimedia content. The result of this feature extraction are 17 low-level descriptors. Here, we used the Audio Power, Audio Harmonicity, and Audio Spectrum Projection features. Moreover, the discrete wavelet transform (DWT) was utilized for audio signal reconstruction. The reconstructed audio signals were classified by the new method, which uses a support vector machine with a confidence interval (SVM-CI). According to the experimental results, the success rate of the proposed method was satisfactory and SVM-CI outperformed the ordinary SVM.

Optimal Representation of Anuran Call Spectrum in Environmental Monitoring Systems Using Wireless Sensor Networks.

The analysis and classification of the sounds produced by certain animal species, notably anurans, have revealed these amphibians to be a potentially strong indicator of temperature fluctuations and therefore of the existence of climate change. Environmental monitoring systems using Wireless Sensor Networks are therefore of interest to obtain indicators of global warming. For the automatic classification of the sounds recorded on such systems, the proper representation of the sound spectrum is essential since it contains the information required for cataloguing anuran calls. The present paper focuses on this process of feature extraction by exploring three alternatives: the standardized MPEG-7, the Filter Bank Energy (FBE), and the Mel Frequency Cepstral Coefficients (MFCC). Moreover, various values for every option in the extraction of spectrum features have been considered. Throughout the paper, it is shown that representing the frame spectrum with pure FBE offers slightly worse results than using the MPEG-7 features. This performance can easily be increased, however, by rescaling the FBE in a double dimension: vertically, by taking the logarithm of the energies; and, horizontally, by applying mel scaling in the filter banks. On the other hand, representing the spectrum in the cepstral domain, as in MFCC, has shown additional marginal improvements in classification performance.

Indexing and Retrieval of Music using Gaussian Mixture Model Techniques

Audio processing systems have taken gigantic leaps in everyday life of most people in developed countries. The technologies are getting entrenched in providing entertainment to consumers. Digital audio techniques have now achieved domination in audio delivery with CD players, internet radio, mp3 players and iPods being the systems of choice in many cases. With the huge growth of the digital music databases people begin to realize the importance of effectively managing music databases relying on music content analysis. The goal of music indexing and retrieval system is to provide the user with capabilities to index and retrieve the music data in an efficient manner. For efficient music retrieval, some sort of music similarity measure is desirable. In this paper, we propose a method for indexing and retrieval of the music using Gaussian mixture models. Acoustic features namely MFCC, chromagram, tempogram and MPEG-7 features are used to create the index. Retrieval is based on the highest probability density function and the experimental analysis shows that the rate of average number of clips retrieved for each query is 5 clips.

Pathological voice detection and binary classification using MPEG-7 audio features

ObjectivesA pathological voice detection and classification method based on MPEG-7 audio low-level features is proposed in this paper. MPEG-7 features are originally used for multimedia indexing, which includes both video and audio. Indexing is related to event detection, and as pathological voice is a separate event than normal voice, we show that MPEG-7 part-4 audio low-level features can do very well in detecting pathological voices, as well as binary classifying the pathologies. Patients and methodsThe experiments are done on a subset of sustained vowel (“AH”) recordings from healthy and voice pathological subjects, from the Massachusetts Eye and Ear Infirmary (MEEI) database. For classification, support vector machine (SVM) is applied. An optional feature selection method, namely, Fisher discrimination ratio is applied. ResultsThe proposed method with MPEG-7 audio features and SVM classification is evaluated on voice pathology detection, as well as binary pathologies classification. The proposed method is able to achieve an accuracy of 99.994% with a standard deviation of 0.0105% for detecting pathological voices and an accuracy up to 100% for binary pathologies classification. ConclusionMPEG-7 descriptors can reliably be used for automatic voice pathology detection and classification.

Urban photograph localization using the INSTREET application—accuracy and performance analysis

The paper proposes a solution to the problem of geolocation of photographs by using an algorithm to compare their content against a geolocated database of street view images, and analyzing the performance of the algorithm. The algorithm makes it possible to pinpoint the location where a photograph was taken. In order to solve this problem, we propose an algorithm based on MPEG-7 features. The paper also describes the results of optimizing the performance of the algorithm and its accuracy. We show that the algorithm scales with the size of the reference database at least up to 130 km2, which was the largest urban area we tested the algorithm on.

Efficient Content based Image Retrieval System using Mpeg-7 Features

This paper gives an overview idea of efficient retrieval of images using different Mpeg-7 Features. Content Based Image Retrieval is a technique of automatic indexing and retrieving of images from a large data base. Feature Extraction and Similarity Matching are the two major steps for CBIR Systems. Color, Texture and Shape represent the three visual features for any image. Mpeg-7 Stands for Multimedia Content Description Interface. The main objective of Mpeg-7 is to provide a standardized set of technologies for describing multimedia content. It has allowed quick and efficient content identification, and addressing a large range of applications. The visual descriptors are classified according to the feature such as color, shape, texture, etc. This paper has used Color Structure Descriptor for color and Edge Histogram Descriptor for texture.These two features are also integrated to increase the performance of CBIR Systems. The efficiency of all methods are demonstrated with the help of results.

Knowledge discovery-based identification of musical pitches and instruments in polyphonic sounds

Pitch and timber detection methods applicable to monophonic digital signals are common. Conversely, successful detection of multiple pitches and timbers in polyphonic time-invariant music signals remains a challenge. A review of these methods, sometimes called “Blind Signal Separation”, is presented in this paper. We analyze how musically trained human listeners overcome resonance, noise, and overlapping signals to identify and isolate what instruments are playing and then what pitch each instrument is playing. The part of the instrument and pitch recognition system, presented in this paper, responsible for identifying the dominant instrument from a base signal uses temporal features proposed by Wieczorkowska [Slezak, D., Synak, P., Wieczorkowska, A., Wróblewski, J., 2002. Kdd-based approach to musical instrument sound recognition. Hacid, M.-S., Raś, Z.W., Zighed, D.A., Kodratoff, Y. (Eds.), Foundations of Intelligent Systems. Proceedings of 13th Symposium ISMIS 2002, Lyon, Franc 4519 Berlin, Heidelberg, pp. 28–36.] in addition to the standard 11 MPEG7 features. After retrieving a semantical match for that dominant instrument from the database, it creates a resulting foreign set of features to form a new synthetic base n signal which no longer bears the previously extracted dominant sound. The system may repeat this process until all recognizable dominant instruments are accounted for in the segment. The proposed methodology incorporates Knowledge Discovery, MPEG7 segmentation and Inverse Fourier Transforms.

Interactive image data labeling using self-organizing maps in an augmented reality scenario

We present an approach for the convenient labeling of image patches gathered from an unrestricted environment. The system is employed for a mobile Augmented Reality (AR) gear: while the user walks around with the head-mounted AR-gear, context-free modules for focus-of-attention permanently sample the most ‘interesting’ image patches. After this acquisition phase, a Self-Organizing Map (SOM) is trained on the complete set of patches, using combinations of MPEG-7 features as a data representation. The SOM allows visualization of the sampled patches and an easy manual sorting into categories. With very little effort, the user can compose a training set for a classifier, thus, unknown objects can be made known to the system. We evaluate the system for COIL-imagery and demonstrate that a user can reach satisfying categorization within few steps, even for image data sampled from walking in an office environment. (An abbreviated version of some portions of this article appeared in [Bekel, H., Heidemann, G., & Ritter, H. (2005). SOM Based Image Data Structuring in an Augmented Reality Scenario. In Proceedings of the International Joint Conference on Neural Networks, Montreal, Canada.], as part of the IJCNN 2005 conference proceedings, published under the IEEE copyright).