Speech coding

Perceptron

Support vector machine

Acoustic model

Telecommunications

Computer science

Audio signal

Audio mining

Mel-frequency cepstrum

Pattern recognition (psychology)

Speech processing

Artificial intelligence

Artificial neural network

Feature extraction

Multilayer perceptron

Speech recognition

Frame (networking)

Preprocessor

Journal of Circuits Systems and Computers

A preprocessing stage in every speech/music applications including audio/speech separation, speech/speaker recognition and audio/genre transcription task is inevitable. The importance of such pre-processing stage is originated from the requisite of determining each frame of the given signal is belonged to which classes, namely: speech only, music only or speech/music mixture. Such classification can significantly decrease the computational burden due to exhaustive search commonly introduced as a problem in model-based speech recognition or separation as well as music transcription scenarios. In this paper, we present a new method to separate mixed type audio frames based on support vector machine (SVM) and neural network. We present a feature type selection algorithm which seeks for the most appropriate features to discriminate possible classes (hypotheses) on the mixed signal. We also propose features based on eigen-decomposition on the mixed frame. Experimental results demonstrate that the proposed features together with the selected audio classifiers achieve acceptable classification results. From the experimental results, it is observed that the proposed system outperforms other classification systems including k-nearest neighbor (k-NN) and multi-layer perceptron (MLP).

AUDIO CLASSIFICATION OF MUSIC/SPEECH MIXED SIGNALS USING SINUSOIDAL MODELING WITH SVM AND NEURAL NETWORK APPROACH