The phrase "audio identification" is correct and usable in written English.
It can be used in contexts related to recognizing or verifying audio signals, sounds, or voices, often in technology or security fields.
Example: "The new software features advanced audio identification capabilities that can distinguish between different speakers."
Alternatives: "sound recognition" or "voice identification."
Google research director Peter Norvig predicts that the prototype, which uses an audio identification technique invented outside Google and applied to a uniquely large database of recorded sound, will eventually evolve into a product.
Audio identification via fingerprint has been an active research field for years.
In this paper, a robust audio identification technique was proposed and compared to the Shazam method [7].
The ORB implementation in this paper is an experimental prototype used to achieve audio identification based on the spectrogram image.
This way, we may seek to explore alternative approaches to audio identification by making recourse to mature technical means applied in computer vision.
Further investigations involving the use of image recognition for audio identification as computer vision techniques made significant progress in recent years with the development of local invariant features.
("Social- and Interactive-Television Applications Based on Real-Time Ambient-Audio Identification" 10 pg PDF, see also the Google Research blog post on the paper).
At minimum, the log will capture audio stream/file identification, user agent, listen start time, listen duration and device location information.
Figure 1 An example of the result of the proposed audio tampering identification, applied to a fragment of speech read from a newspaper.
Motivated by the human auditory algorithm, Sukittanon et al. [12] proposed to use long-term modulation scale features for audio content identification.
FFTs are used extensively in a wide range of digital signal processing applications, including spectrum analysis, high-speed convolution (linear filtering), filter banks, signal detection and estimation, system identification, audio compression (e.g., MPEG-II AAC), spectral modeling sound synthesis, and many other applications; some of these will be discussed in Chapter 8.
