Audio feature extraction for classification using relative transformation
Guihua Wen, Jian Tuo, Lijun Jiang, Jia Wei · 2012
Audio feature extraction plays a much important role in the areas of audio processing. This paper proposes a new audio feature extraction method using the relative transformation (RT). It begins with equally dividing an audio signal into a lot of segments. On each segment, the mel-frequency cepstral coefficients are extracted and combined by RT to generate a single feature vector. All these vectors are then combined by RT again to generate a single feature vector for the audio. This method can nicely deal with the noisy, sparse, and imbalance problems, while it has lower time complexity. It is purely data-driven and does not depend on particular audio characteristics. The experimental results suggest that the classifier with the proposed method often gives the better results in classification.