Fundamentals of speech recognition
Fundamentals of speech recognition
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
The ToCAI Description Scheme for Indexing and Retrieval of Multimedia Documents
Multimedia Tools and Applications
Video Handling with Music and Speech Detection
IEEE MultiMedia
Construction and Evaluation of a Robust Multifeature Speech/Music Discriminator
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 2 - Volume 2
Real-time discrimination of broadcast speech/music
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 02
Hierarchical classification of audio data for archiving and retrieving
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 06
Describing multimedia documents in natural and semantic-driven ordered hierarchies
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 04
A decision-tree-based algorithm for speech/music classification and segmentation
EURASIP Journal on Audio, Speech, and Music Processing
Online speech/music segmentation based on the variance mean of filter bank energy
EURASIP Journal on Advances in Signal Processing
Digital Signal Processing
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We focus the attention on the problem of audio classification in speech and music for multimedia applications. In particular, we present a comparison between two different techniques for speech/music discrimination. The first method is based on zero crossing rate and Bayesian classification. It is very simple from a computational point of view, and gives good results in case of pure music or speech. The simulation results show that some performance degradation arises when the music segment contains also some speech superimposed on music, or strong rhythmic components. To overcome these problems, we propose a second method, that uses more features, and is based on neural networks (specifically a multi-layer Perceptron). In this case we obtain better performance, at the expense of a limited growth in the computational complexity. In practice, the proposed neural network is simple to be implemented if a suitable polynomial is used as the activation function, and a real-time implementation is possible even if low-cost embedded systems are used.