Review on Design and Implementation of Audio Signal Classification System to classify the Media in Speech/Music (original) (raw)
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AN EFFICIENT FEATURE SELECTION IN CLASSIFICATION OF AUDIO FILES
In this paper we have focused on an efficient feature selection method in classification of audio files. The main objective is feature selection and extraction. We have selected a set of features for further analysis, which represents the elements in feature vector. By extraction method we can compute a numerical representation that can be used to characterize the audio using the existing toolbox. In this study Gain Ratio (GR) is used as a feature selection measure. GR is used to select splitting attribute which will separate the tuples into different classes. The pulse clarity is considered as a subjective measure and it is used to calculate the gain of features of audio files. The splitting criterion is employed in the application to identify the class or the music genre of a specific audio file from testing database. Experimental results indicate that by using GR the application can produce a satisfactory result for music genre classification. After dimensionality reduction best three features have been selected out of various features of audio file and in this technique we will get more than 90% successful classification result.
Automatic Classification of Audio Data
Systems, Man and …, 2005
In this paper a novel content-based musical genre classification approach that uses combination of classifiers is proposed. First, musical surface features and beatrelated features are extracted from different segments of digital music in MP3 format. Three 15-dimensional feature vectors are extracted from three different parts of a music clip and three different classifiers are trained with such feature vectors. At the classification mode, the outputs provided by the individual classifiers are combined using a majority vote rule. Experimental results show that the proposed approach that combines the output of the classifiers achieves higher correct musical genre classification rate than using single feature vectors and single classifiers.
Features and classifiers for the automatic classification of musical audio signals
2004
Several factors affecting the automatic classification of musical audio signals are examined. Classification is performed on short audio frames and results are reported as "bag of frames" accuracies, where the audio is segmented into 23ms analysis frames and a majority vote is taken to decide the final classification. The effect of different parameterisations of the audio signal is examined. The effect of the inclusion of information on the temporal variation of these features is examined and finally, the performance of several different classifiers trained on the data is compared. A new classifier is introduced, based on the unsupervised construction of decision trees and either linear discriminant analysis or a pair of single Gaussian classifiers. The classification results show that the topology of the new classifier gives it a significant advantage over other classifiers, by allowing the classifier to model much more complex distributions within the data than Gaussian schemes do.
Feature Analysis for Audio Classification
Lecture Notes in Computer Science, 2014
In this work we analyze and implement several audio features. We emphasize our analysis on the ZCR feature and propose a modification making it more robust when signals are near zero. They are all used to discriminate the following audio classes: music, speech, environmental sound. An SVM classifier is used as a classification tool, which has proven to be efficient for audio classification. By means of a selection heuristic we draw conclusions of how they may be combined for fast classification.
Audio classification utilizing a rule-based approach and the support vector machine classifier
2013
The evaluation of two classification architectures utilizing the rule-based approach and the one-against-one support vector machine (OAO-SVM) is presented in this paper. The classification of the audio stream is carried out in two steps. At first, the rule-based speech/non-speech and music/environment sound discrimination is conducted. The set of adopted features, with a high efficiency in separation of speech and music signals, is implemented in order to find the best discriminator. Consequently, speech segments are classified into pure speech, speech with music and speech with env. sound using the OAO-SVM multi-class classification scheme. Experimental results show that the used classification architecture can decrease the classification error in comparison with OAO-SVM by using MFCC features only.
Classification of Audio Data using Support Vector Machine
2011
Audio mining is to extract audio signals for indicating patterns and features of audio data to get data mining results. Various audio features like Mel frequency Cepstral Coefficient (MFCC), Linear Predictive Coefficient (LPC), Compactness, Spectral Flux (SF), Band Periodicity (BP), Zero Crossing Rate (ZCR) etc are used to classify audio data into various classes. Various classification algorithms such as Naive Bayes, FT, J48, ID3 and LibSVM are used to classify audio data into defined classes. Using various performance parameters such as True Positive (TP) Rate, False Positive (FP) Rate etc., results of various classification algorithms are compared.
Content-Based Audio Classification and Retrieval: A Novel Approach
The amount of audio data on public networks like Internet is increasing in huge volume daily. So to access these media, we need to efficiently index and annotate them. Due to non-stationary nature and discontinuities present in the audio signal, segmentation and classification of audio signal has really become a challenging task. Automatic music classification and annotation is also one of the challenging tasks due to the difficulty in extracting and selecting optimal audio features. Today, content-based audio retrieval systems are used in various application domains and scenarios such as music retrieval, speech recognition, and acoustic surveillance. During the development of an audio retrieval system, a major challenge is the identification of appropriate contentbased features for representation of the audio signals under consideration. This paper gives the overview of various techniques used for classification and retrieval of audio and also proposes a novel approach for classification and retrieval of audio signal.
The volume of audio data is increasing tremendously daily on public networks like Internet. This increases the difficulty in accessing those audio data. Hence, there is a need of efficient indexing and annotation mechanisms. The problems like non-stationarities and discontinuities present in the audio signal rises the difficulty in segmentation and classification of audio signals. The other challenging task is the extracting and selecting the optimal features in audio signal. The application areas of audio classification and retrieval system includes speaker recognition, gender classification, music genre classification, environment sound classification, etc. This paper proposes a machine learning approach based on neural network which performs audio pre-processing, segmentation, feature extraction, classification and retrieval of audio signal from the dataset. We found that FPNN classifier gives better accuracy, F1-score and Kappa coefficient values compared to SVM, k-NN and PNN classifiers.
Applying neural network on the content-based audio classification
Fourth International Conference on Information, Communications and Signal Processing, 2003 and the Fourth Pacific Rim Conference on Multimedia. Proceedings of the 2003 Joint
Many audio and multimedia applications would benefit if they could interpret the content of audio rather than relying on descriptions or keywords. These applications include multimedia databases and file systems, digital libraries, automatic segmentation or indexing of video (e.g., news or sports storage), and surveillance. This paper describes a novel content-based audio classification approach based on neural network and genetic algorithm. Experiments show this approach achieves a good performance of the classification.