Acoustic fingerprint
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위키데이터
- ID : Q1807085
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- Media identification using acoustic fingerprints can be used to monitor the use of specific musical works and performances on radio broadcast, records, CDs, streaming media and peer-to-peer networks.[1]
- A robust acoustic fingerprint algorithm must take into account the perceptual characteristics of the audio.[1]
- If two files sound alike to the human ear, their acoustic fingerprints should match, even if their binary representations are quite different.[1]
- Acoustic fingerprints are not hash functions, which must be sensitive to any small changes in the data.[1]
- Below code snippet shows how to extract acoustic fingerprints from an audio file and later use them as identifiers to recognize unknown audio query.[2]
- If you plan to use an external persistent storage for audio fingerprints Emy is the preferred choice.[2]
- It is a specialized storage developed for audio fingerprints.[2]
- You provide a file at the input, and after a certain number of conversions, you get “audio fingerprints” at the output.[3]
- Around the year 2000, the first ideas were coined to create audio fingerprints.[4]
- Practically all audio fingerprints are based on features in a spectrogram.[4]
- This paper presents an implementation of Artificial Neural Networks (ANN) for acoustic fingerprints recognition, applied to the identification of marine vessels.[5]
- It seems that the last one is currently the right way, and technologies such as acoustic fingerprint may allow us to provide such monitoring and enforcement.[6]
- Various pieces of pipeline equipment (especially those with rotating parts such as pumps, valves or generators) produce strong acoustic fingerprints .[7]
- It also claims that Apple will bring back the Touch ID in a new avatar, that is, an acoustic fingerprint technology that could allow for full-screen Touch ID.[7]
- At the core of AcoustID is an efficient algorithm for extracting audio fingerprints, called Chromaprint.[8]
- The algorithm is optimized specifically for matching near-identical audio streams, which allows the audio fingerprints to be very compact and the extraction process to be fast.[8]
- AcoustID contains a large crowd-sourced database of such audio fingerprints together with additional information about them, such as the song title, artist or links to the MusicBrainz database.[8]
- You can send an audio fingerprint to the AcoustID service and it will search the database and return you information about the song.[8]
- 3rd-generation algorithm using Acoustic Fingerprint Technology (AFT) to mark the outgoing signal.[9]
- Siemens FeedbackStopper is an adaptive phase cancellation system combined with Acoustic Fingerprint Technology and transient frequency shift.[9]
- To distinguish between feedback situations and non-feedback situations, FeedbackStopper employs the Siemens patented Acoustic Fingerprint Technology (AFT).[9]
- Once the acoustic fingerprint is detected (eg, feedback is likely to occur) in a particular acoustic situation, FeedbackStopper briefly shifts the entire output of the amplifier by 25 Hz.[9]
- With Wikipedia Zero becoming a piracy host (T129845) we should consider implementing an acoustic fingerprint matching system (AcoustID, Echoprint, Gracenote MusicID, Shazam) to quickly alert admins.[10]
- Different techniques have been applied to create audio fingerprints; however, with the introduction of deep learning, new data-driven unsupervised approaches are available.[11]
- The generating and storing of time-indexed audio fingerprints are redundant if an assumption may be made as to the portion of the audio piece that will be available for fingerprinting.[12]
- The fingerprint analysis engine 20 analyzes an audio fingerprint generated by the fingerprint extraction engine 18 for a match against registered fingerprints in a fingerprint database 26.[12]
- 2 is a flow diagram of a process for generating an audio fingerprint according to one embodiment of the invention.[12]
- Unlike many audio fingerprints generated by prior art systems, the audio fingerprint generated via the SVD operation has no notion of time associated with it.[12]
소스
- ↑ 1.0 1.1 1.2 1.3 Acoustic fingerprint
- ↑ 2.0 2.1 2.2 AddictedCS/soundfingerprinting: Open source audio fingerprinting in .NET. An efficient algorithm for acoustic fingerprinting written purely in C#.
- ↑ How does Audio Fingerprinting work
- ↑ 4.0 4.1 A Fingerprint for Audio
- ↑ Acoustic Fingerprint Recognition Using Artificial Neural Networks
- ↑ Describing Acoustic Fingerprint Technology Integration For Audio Monitoring Systems
- ↑ 7.0 7.1 Acoustic fingerprint
- ↑ 8.0 8.1 8.2 8.3 Open source audio identification services
- ↑ 9.0 9.1 9.2 9.3 Combining Phase Cancellation, Frequency Shifting, and Acoustic Fingerprint for Improved Feedback Suppression
- ↑ ⚓ T132650 Copyright detection (acoustic fingerprint matching) for audio files
- ↑ SAMAF: Sequence-to-sequence Autoencoder Model for Audio Fingerprinting: ACM Transactions on Multimedia Computing, Communications, and Applications: Vol 16, No 2
- ↑ 12.0 12.1 12.2 12.3 US7013301B2 - Audio fingerprinting system and method - Google Patents
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위키데이터
- ID : Q1807085
Spacy 패턴 목록
- [{'LOWER': 'acoustic'}, {'LEMMA': 'fingerprint'}]