PatentDOI
System and methods for recognizing sound and music signals in high noise and distortion
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TLDR
In this article, the authors proposed a method for recognizing audio samples that locates an audio file that most closely matches the audio sample from a database indexing a large set of original recordings, where each indexed audio file is represented in the database index by a set of landmark timepoints and associated fingerprints.Abstract:
A method for recognizing an audio sample locates an audio file that most closely matches the audio sample from a database indexing a large set of original recordings. Each indexed audio file is represented in the database index by a set of landmark timepoints and associated fingerprints. Landmarks occur at reproducible locations within the file, while fingerprints represent features of the signal at or near the landmark timepoints. To perform recognition, landmarks and fingerprints are computed for the unknown sample and used to retrieve matching fingerprints from the database. For each file containing matching fingerprints, the landmarks are compared with landmarks of the sample at which the same fingerprints were computed. If a large number of corresponding landmarks are linearly related, i.e., if equivalent fingerprints of the sample and retrieved file have the same time evolution, then the file is identified with the sample. The method can be used for any type of sound or music, and is particularly effective for audio signals subject to linear and nonlinear distortion such as background noise, compression artifacts, or transmission dropouts. The sample can be identified in a time proportional to the logarithm of the number of entries in the database; given sufficient computational power, recognition can be performed in nearly real time as the sound is being sampled.read more
Citations
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References
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Journal ArticleDOI
Content-based classification, search, and retrieval of audio
TL;DR: The audio analysis, search, and classification engine described here reduces sounds to perceptual and acoustical features, which lets users search or retrieve sounds by any one feature or a combination of them, by specifying previously learned classes based on these features.
Proceedings Article
Finding similar files in a large file system
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Patent
Method and article of manufacture for content-based analysis, storage, retrieval, and segmentation of audio information
TL;DR: In this paper, a system that performs analysis and comparison of audio data files based upon the content of the data files is presented, which produces a set of numeric values (a feature vector) that can be used to classify and rank the similarity between individual audio files typically stored in a multimedia database or on the Web.
Proceedings ArticleDOI
Content-based retrieval of music and audio
TL;DR: In this article, a supervised vector quantizer is used to learn audio features from a corpus of simple sounds and musical excerpts, and the similarity measure is based on statistics derived from a supervised quantizer, rather than matching simple pitch or spectral characteristics.
Journal ArticleDOI
An overview of audio information retrieval
TL;DR: The state of the art in audio information retrieval is reviewed, and recent advances in automatic speech recognition, word spotting, speaker and music identification, and audio similarity are presented with a view towards making audio less “opaque”.