The phrase "audio extraction" is correct and usable in written English.
It can be used in contexts related to the process of retrieving audio from a source, such as a video or a file.
Example: "The software allows for easy audio extraction from video files, making it convenient for podcast creators."
Alternatives: "sound extraction" or "audio retrieval".
Audio feature extraction plays an important role in analyzing and characterizing audio content.
Section 5 describes the processes of spectrogram image construction and robust audio fingerprint extraction.
The audio feature extraction process is briefly described in Section 3. In Section 4, the LRSMs are detailed.
This would be due to the peaks being acquired using the spectrogram data in the audio fingerprinting extraction process.
Two separate audio feature extraction approaches are used to evaluate the performance of the feature synthesis with different types of features.
Unlike the conventional audio feature extraction modules that mostly operates in Mel scale, in which speech contents are efficiently modeled rather than emotion, we propose working on perceptual spectrums derived in Bark scale.
Audio feature extraction serves as the basis for a wide range of applications in the areas of speech processing [37], multimedia data management and distribution [38 41], security [42], biometrics and bioacoustics [43].
Unfortunately, despite the enormous number of different audio feature extraction methods available in the literature, the features have limitations and drawbacks in describing the data content, so that the current audio classifiers cannot really compete with the human auditory perception system.
Unlike compaction in audio coding or extraction of information in classification, to protect the digital audio content addition of information in the form of a security key is required which would then prove the ownership of the audio content.
