A new approach for audio classification and segmentation using Gabor wavelets and Fisher linear discriminator

Rapid increase in the amount of audio data demands an efficient method to automatically segment or classify audio stream based on its content. In this paper, based on the Gabor wavelet features, an audio classification and segmentation method is proposed. This method will first divide an audio stream into clips, each of which contains one-second audio information. Then, each clip is classified as one of two classes or five classes. Two classes contain speech and music; pure speech, pure music, song, speech with music background, and speech with environmental noise background are for five classes. Finally, a merge technique is provided to do segmentation. In order to make the proposed method robust for a variety of audio sources, we use Fisher Linear Discriminator to obtain features with the highest discriminative ability. Experimental results show that the proposed method can achieve over 98% accuracy rate for speech and music discrimination, and more than 95% for a five-way discrimination. By checking the class types of adjacent clips, we can also identify more than 95% audio scene breaks in audio sequence.