Sentence examples for classification of audio from inspiring English sources

The presented work focuses on one such way for automatic classification of audio signals for retrieval purposes.

Chen [32] proposes a rule-based method for classification of audio data into one of the following emotion categories: happiness, sadness, fear, anger, surprise, and dislike.

For the classification of audio signals, we employ the standard method of voice activity detection based on the zero crossing rate (ZCR) and energy [25].

However, Section Classification of audio steganography methodsClassificationof audio steganography methods proposes a classification of existing audio steganographic techniques based on their occurrence instances in voice encoders.

In [24], it is shown that the NMF decomposition method of a TF spectrum is superior to PCA and ICA for classification of audio data.

Initial classification of audio segments into broad categories such as speech, non-speech, and silence provides useful information for audio content understanding and analysis [1], and it has been used in a variety of commercial, forensic, and military applications [2].

In line with the above 3 application areas, this paper presents and discusses a TF-based audio coding scheme, music classification, audio classification of environmental sounds, audio fingerprinting, and audio watermarking.

A TF-based audio coding scheme with novel psychoacoustics model, music classification, audio classification of environmental sounds, audio fingerprinting, and audio watermarking will be presented to demonstrate the advantages of using time-frequency approaches in analyzing and extracting information from audio signals.

To our point of view, previous approaches focus on different aspects of classification of general audio events trying to optimize a specific part of the problem and are mostly laboratory based experiments, that is, prerecorded well defined classes are presented to a classification algorithm.

The proposed training method does not require prior segmentation or classification of the audio-visual data, avoiding in this way the errors derived from these classification procedures.

Some approaches not requiring an a priori classification of the audio-visual data for the training stage have been also proposed in the literature.