Classification of audio signals using AANN and GMM

Authors:
P. Dhanalakshmi;S. Palanivel;V. Ramalingam
Affiliations:
Department of Computer Science and Engineering, Annamalai University, Annamalainagar 608 002, India;Department of Computer Science and Engineering, Annamalai University, Annamalainagar 608 002, India;Department of Computer Science and Engineering, Annamalai University, Annamalainagar 608 002, India
Venue:
Applied Soft Computing
Year:
2011

Citing 12
Cited 3

Classification of general audio data for content-based retrieval

Pattern Recognition Letters - Special issue on image/video indexing and retrieval
Neural Networks: A Comprehensive Foundation

Neural Networks: A Comprehensive Foundation
AANN: an alternative to GMM for pattern recognition

Neural Networks
Speech/music segmentation using entropy and dynamism features in a HMM classification framework

Speech Communication
Artificial Neural Networks

Artificial Neural Networks
Audio-based context recognition

IEEE Transactions on Audio, Speech, and Language Processing
Advances in unsupervised audio classification and segmentation for the broadcast news and NGSW corpora

IEEE Transactions on Audio, Speech, and Language Processing
A generic audio classification and segmentation approach for multimedia indexing and retrieval

IEEE Transactions on Audio, Speech, and Language Processing
Audio Signal Feature Extraction and Classification Using Local Discriminant Bases

IEEE Transactions on Audio, Speech, and Language Processing
A speech/music discriminator based on RMS and zero-crossings

IEEE Transactions on Multimedia
Multigroup classification of audio signals using time-frequency parameters

IEEE Transactions on Multimedia
Content-based audio classification and retrieval by support vector machines

IEEE Transactions on Neural Networks

Dynamic and scalable audio classification by collective network of binary classifiers framework: An evolutionary approach

Neural Networks
Intelligent acoustic rotor speed estimation for an autonomous helicopter

Applied Soft Computing
Designing smart cities: security issues

CISIM'12 Proceedings of the 11th IFIP TC 8 international conference on Computer Information Systems and Industrial Management

Quantified Score

Hi-index	0.00

Visualization

Abstract

Today, digital audio applications are part of our everyday lives. Audio classification can provide powerful tools for content management. If an audio clip automatically can be classified it can be stored in an organised database, which can improve the management of audio dramatically. In this paper, we propose effective algorithms to automatically classify audio clips into one of six classes: music, news, sports, advertisement, cartoon and movie. For these categories a number of acoustic features that include linear predictive coefficients, linear predictive cepstral coefficients and mel-frequency cepstral coefficients are extracted to characterize the audio content. The autoassociative neural network model (AANN) is used to capture the distribution of the acoustic feature vectors. The AANN model captures the distribution of the acoustic features of a class, and the backpropagation learning algorithm is used to adjust the weights of the network to minimize the mean square error for each feature vector. The proposed method also compares the performance of AANN with a Gaussian mixture model (GMM) wherein the feature vectors from each class were used to train the GMM models for those classes. During testing, the likelihood of a test sample belonging to each model is computed and the sample is assigned to the class whose model produces the highest likelihood.